DOCSLIB.ORG

Truncated SVD-Based Feature Engineering for Music Recommendation KKBOX’S Music Recommendation Challenge at ACM WSDM Cup 2018

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Truncated SVD-Based Feature Engineering for Music Recommendation KKBOX’S Music Recommendation Challenge at ACM WSDM Cup 2018 Truncated SVD-based Feature Engineering for Music Recommendation KKBOX’s Music Recommendation challenge at ACM WSDM Cup 2018 Nima Shahbazi Mohamed Chahhou Jarek Gryz York University Sidi Mohamed Ben Abdellah York University [email protected] University [email protected] [email protected] ABSTRACT to suggest relevant preference to a specific user. Many web ser- 1 This year’s ACM WSDM Cup asked the ML community to build an vices such as Netflix, Spotify, YouTube, KKBOX use CF to deliver improved music recommendation system using a dataset provided personalized recommendation to their customers. by KKBOX. The task was to predict the chances a user would listen The ACM WSDM Cup challenged the ML community to improve to a song repetitively after the first observable listening event within KKBOX model and build a better music recommendation system 2 a given time frame. We cast this problem as a binary classification using their dataset . KKBOX currently uses CF based algorithm problem and addressed it by using gradient boosted decision trees. with matrix factorization but expects that other techniques could To overcome the cold start problem, which is a notorious problem in lead to better music recommendations. recommender systems, we create truncated SVD-based embedding In this task, we want to predict songs which a user will truly like. features for users, songs and artists. Using the embedding features Intuitively, if a user much enjoys a song, s/he will repetitively listen with four different statistical based features (users, songs, artists to it. We are asked to predict the chances of a user listening to a song and time), our model won the ACM challenge, ranking second. repetitively after the first observable listening event within a given There was no music domain knowledge needed for creating the time window. If there are recurring listening event(s) triggered features, and we only relied on the information gain and prediction within a month after the user’s very first observable listening event, accuracy for feature selection. its target is marked 1, and 0 otherwise. More formally, let’s assume an event E¹U ; S;T1º in which a user U listened to a song S and it 0 occurred at time T1. If we observe a subsequent event E ¹U ; S;T2º CCS CONCEPTS where T 2−T 1 < 1 month (that is, we got repetitive listening within • Information systems → Recommender systems; one month), event E will be marked as 1 otherwise it will be marked as 0. The submissions are evaluated on area under the ROC curve between the predicted probability and the observed target. KEYWORDS For this competition, KKBOX has provided a training data set Truncated SVD, Cold start, Gradient boosting that consists of information of the first observable listening event for each unique user-song pair (E) within a specific timeframe. The training and the test data are selected from users’ listening history in a given time period and have around 7 and 2.5 millions unique 1 INTRODUCTION user-song pairs respectively. Although the training and the test sets are split based on time and ordered chronologically, the timestamps Until recently, people listened to their own local music library and for train and test are not provided. It is worth mentioning that this rarely curated collections online. But the era of local music libraries structure also suffers from the cold start problem: 14:5% of the is over. Personalization algorithms and unlimited streaming ser- users and 26:6% of the songs in test do not appear in the training vices like YouTube, Spotify, etc. are emerging. People now listen data. Table 1 contains some statistics on users and songs in the to all kinds of music and algorithms still struggle in some key ar- training and test data provided by KKBOX which clearly shows eas. Without enough historical data, how can an algorithm predict the existence of the cold start problem. Hence the main task is to whether a listener will like a new song or a new artist? And how predict whether or not a new listener will like a new song or a can it recommend songs to brand new users? new artist. We used embedded features to overcome this problem The popularity of online content services and social media has (described in detail in Section 2.1). demonstrated the value of providing relevant information to users. Recommender systems have proven to be an effective tool for this purpose and are receiving increasingly more attention. Also, re- cently the amount of available training data has increased enor- mously and advances in hardware (like GPUs) have made it possible to tackle these problems in a reasonable amount of time. One common approach for building an accurate recommender 1KKBOX is an Asia’s leading music streaming service, holding the world’s most model is collaborate filtering (CF). CF, used widely across various comprehensive Asia-Pop music library with over 30 million tracks. domains [9], exploits the overall behavior or taste of other users 2https://www.kaggle.com/c/kkbox-music-recommendation-challenge/data Table 1: users and songs distribution in train and test set Train Test 7.377.418 events 2.556.790 events 30.755 unique users 25.131 unique users 359.966 unique songs 224.753 unique songs 9.272 users are in train but not in test 3.648 users are in test but not in train; this corresponds to 14.51% new users 195.086 songs are in train but not in test 59.873 songs are in test but not in train; this corresponds to 26.64% new songs 2 OUR APPROACH The performance of any supervised learning model relies on two principal factors: predictive features and effective learning algo- rithm. The feature engineering approach exploits the domain knowl- edge to extract features from the training data set that should gen- eralize well to the unseen data in the test set. The features are in general implicit information contained in the training data set which the algorithms are not able to extract by themselves. The quality and quantity of the features have a direct impact on the overall quality of the model. Finding the relevant features requires understanding and subse- quent analysis of the problem structure. Judging the quality of a new feature is done by examining the information gain of the feature Figure 1: Evolution of repeated listening (target) in time. first and then comparing the performance of the model before and after the feature is added. Unfortunately, finding such good features is not an easy task and is also computationally expensive. Most In Figure 3, we plot the same distribution as above but for the of the time, the engineered features lead to only small improve- last 1 million observations in the train dataset and we noticed a ments in the performance of the model; to achieve a noticeable huge drop of re-listening in local_library and local_playlist. improvement requires engineering of hundreds of features. Also, the online_playlist counts have increased significantly. Table 2 summarize all features provided by KKBOX. In this work, It seems that users behaviors have changed over time by online we developed a set of hand-crafted features which can be grouped users who are more interested in what other users listened to via into the following classes: session features, user-based statistical fea- the online_playlist. As a result, they are more interested in tures, song-based statistical features, artist-based statistical features discovering new songs rather than re-listening again to what they and embedding features based on user, song, artist and meta-data already liked. Still, we can see from the figure that listening to what from the dataset. These features combined with our learning algo- other people like does not guarantee that they will re-listen to the rithm proved to be quite powerful and gave us the second-place song which explains why the target mean drops over time. ranking with the score difference of 0.00094 from the first place. 2.1.1 Session Features. The provided dataset does not include 2.1 Feature Engineering any timestamps or user session information. However, since the data is ordered chronologically, we were able to approximate user Before turning into feature engineering problem, we would like sessions using the following approach: to highlight some important aspects of our analysis. We noticed (1) merge the train and test dataset that statistical features based on the target feature did not work (2) groupby users well enough during the training stage. This is probably due to the (3) for each user shift the data by one to the right dynamics involved in the data structure and the sampling strategy (4) take the difference between the original and shifted indices made by KKBOX as shown in Figure 1. Figure 1 shows the evolution and call it diff_ind of the target mean over time. The plot is set up by aggregating (5) small diff_ind values3 correspond to observations within observations target mean in bins of size 100000. In can be seen from the same session and high values corresponds to the start of the figure that the target distribution is significantly decreasing a new session over time and consequently we should expect a target mean decline (6) consequently diff_ind value for the start of a new session in the test dataset too. is assigned for the entire session Further analysis of the given features shows a strong correlation between source_type feature and the target value. In Figure 2, we For example, suppose that user X has {1,2,3,4,20,21,23,26,100,105,111} plot the distribution of songs count per source_type categories indices in the data set.
Load more

Recommended publications
  • Incorporating Automated Feature Engineering Routines Into Automated Machine Learning Pipelines by Wesley Runnels
    Incorporating Automated Feature Engineering Routines into Automated Machine Learning Pipelines by Wesley Runnels Submitted to the Department of Electrical Engineering and Computer Science in partial fulfillment of the requirements for the degree of Master of Engineering in Electrical Engineering and Computer Science at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY May 2020 c Massachusetts Institute of Technology 2020. All rights reserved. Author . Wesley Runnels Department of Electrical Engineering and Computer Science May 12th, 2020 Certified by . Tim Kraska Department of Electrical Engineering and Computer Science Thesis Supervisor Accepted by . Katrina LaCurts Chair, Master of Engineering Thesis Committee 2 Incorporating Automated Feature Engineering Routines into Automated Machine Learning Pipelines by Wesley Runnels Submitted to the Department of Electrical Engineering and Computer Science on May 12, 2020, in partial fulfillment of the requirements for the degree of Master of Engineering in Electrical Engineering and Computer Science Abstract Automating the construction of consistently high-performing machine learning pipelines has remained difficult for researchers, especially given the domain knowl- edge and expertise often necessary for achieving optimal performance on a given dataset. In particular, the task of feature engineering, a key step in achieving high performance for machine learning tasks, is still mostly performed manually by ex- perienced data scientists. In this thesis, building upon the results of prior work in this domain, we present a tool, rl feature eng, which automatically generates promising features for an arbitrary dataset. In particular, this tool is specifically adapted to the requirements of augmenting a more general auto-ML framework. We discuss the performance of this tool in a series of experiments highlighting the various options available for use, and finally discuss its performance when used in conjunction with Alpine Meadow, a general auto-ML package.
    [Show full text]
  • Learning from Few Subjects with Large Amounts of Voice Monitoring Data
    Learning from Few Subjects with Large Amounts of Voice Monitoring Data Jose Javier Gonzalez Ortiz John V. Guttag Robert E. Hillman Daryush D. Mehta Jarrad H. Van Stan Marzyeh Ghassemi Challenges of Many Medical Time Series • Few subjects and large amounts of data → Overfitting to subjects • No obvious mapping from signal to features → Feature engineering is labor intensive • Subject-level labels → In many cases, no good way of getting sample specific annotations 1 Jose Javier Gonzalez Ortiz Challenges of Many Medical Time Series • Few subjects and large amounts of data → Overfitting to subjects Unsupervised feature • No obvious mapping from signal to features extraction → Feature engineering is labor intensive • Subject-level labels Multiple → In many cases, no good way of getting sample Instance specific annotations Learning 2 Jose Javier Gonzalez Ortiz Learning Features • Segment signal into windows • Compute time-frequency representation • Unsupervised feature extraction Conv + BatchNorm + ReLU 128 x 64 Pooling Dense 128 x 64 Upsampling Sigmoid 3 Jose Javier Gonzalez Ortiz Classification Using Multiple Instance Learning • Logistic regression on learned features with subject labels RawRaw LogisticLogistic WaveformWaveform SpectrogramSpectrogram EncoderEncoder RegressionRegression PredictionPrediction •• %% Positive Positive Ours PerPer Window Window PerPer Subject Subject • Aggregate prediction using % positive windows per subject 4 Jose Javier Gonzalez Ortiz Application: Voice Monitoring Data • Voice disorders affect 7% of the US population • Data collected through neck placed accelerometer 1 week = ~4 billion samples ~100 5 Jose Javier Gonzalez Ortiz Results Previous work relied on expert designed features[1] AUC Accuracy Comparable performance Train 0.70 ± 0.05 0.71 ± 0.04 Expert LR without Test 0.68 ± 0.05 0.69 ± 0.04 task-specific feature Train 0.73 ± 0.06 0.72 ± 0.04 engineering! Ours Test 0.69 ± 0.07 0.70 ± 0.05 [1] Marzyeh Ghassemi et al.
    [Show full text]
  • Expert Feature-Engineering Vs. Deep Neural Networks: Which Is Better for Sensor-Free Affect Detection?
    Expert Feature-Engineering vs. Deep Neural Networks: Which is Better for Sensor-Free Affect Detection? Yang Jiang1, Nigel Bosch2, Ryan S. Baker3, Luc Paquette2, Jaclyn Ocumpaugh3, Juliana Ma. Alexandra L. Andres3, Allison L. Moore4, Gautam Biswas4 1 Teachers College, Columbia University, New York, NY, United States [email protected] 2 University of Illinois at Urbana-Champaign, Champaign, IL, United States {pnb, lpaq}@illinois.edu 3 University of Pennsylvania, Philadelphia, PA, United States {rybaker, ojaclyn}@upenn.edu [email protected] 4 Vanderbilt University, Nashville, TN, United States {allison.l.moore, gautam.biswas}@vanderbilt.edu Abstract. The past few years have seen a surge of interest in deep neural net- works. The wide application of deep learning in other domains such as image classification has driven considerable recent interest and efforts in applying these methods in educational domains. However, there is still limited research compar- ing the predictive power of the deep learning approach with the traditional feature engineering approach for common student modeling problems such as sensor- free affect detection. This paper aims to address this gap by presenting a thorough comparison of several deep neural network approaches with a traditional feature engineering approach in the context of affect and behavior modeling. We built detectors of student affective states and behaviors as middle school students learned science in an open-ended learning environment called Betty’s Brain, us- ing both approaches. Overall, we observed a tradeoff where the feature engineer- ing models were better when considering a single optimized threshold (for inter- vention), whereas the deep learning models were better when taking model con- fidence fully into account (for discovery with models analyses).
    [Show full text]
  • Deep Learning Feature Extraction Approach for Hematopoietic Cancer Subtype Classification
    International Journal of Environmental Research and Public Health Article Deep Learning Feature Extraction Approach for Hematopoietic Cancer Subtype Classification Kwang Ho Park 1 , Erdenebileg Batbaatar 1 , Yongjun Piao 2, Nipon Theera-Umpon 3,4,* and Keun Ho Ryu 4,5,6,* 1 Database and Bioinformatics Laboratory, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea; [email protected] (K.H.P.); [email protected] (E.B.) 2 School of Medicine, Nankai University, Tianjin 300071, China; [email protected] 3 Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand 4 Biomedical Engineering Institute, Chiang Mai University, Chiang Mai 50200, Thailand 5 Data Science Laboratory, Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh 700000, Vietnam 6 Department of Computer Science, College of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea * Correspondence: [email protected] (N.T.-U.); [email protected] or [email protected] (K.H.R.) Abstract: Hematopoietic cancer is a malignant transformation in immune system cells. Hematopoi- etic cancer is characterized by the cells that are expressed, so it is usually difficult to distinguish its heterogeneities in the hematopoiesis process. Traditional approaches for cancer subtyping use statistical techniques. Furthermore, due to the overfitting problem of small samples, in case of a minor cancer, it does not have enough sample material for building a classification model. Therefore, Citation: Park, K.H.; Batbaatar, E.; we propose not only to build a classification model for five major subtypes using two kinds of losses, Piao, Y.; Theera-Umpon, N.; Ryu, K.H.
    [Show full text]
  • Feature Engineering for Predictive Modeling Using Reinforcement Learning
    The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) Feature Engineering for Predictive Modeling Using Reinforcement Learning Udayan Khurana Horst Samulowitz Deepak Turaga [email protected] [email protected] [email protected] IBM Research AI IBM Research AI IBM Research AI Abstract Feature engineering is a crucial step in the process of pre- dictive modeling. It involves the transformation of given fea- ture space, typically using mathematical functions, with the objective of reducing the modeling error for a given target. However, there is no well-defined basis for performing effec- tive feature engineering. It involves domain knowledge, in- tuition, and most of all, a lengthy process of trial and error. The human attention involved in overseeing this process sig- nificantly influences the cost of model generation. We present (a) Original data (b) Engineered data. a new framework to automate feature engineering. It is based on performance driven exploration of a transformation graph, Figure 1: Illustration of different representation choices. which systematically and compactly captures the space of given options. A highly efficient exploration strategy is de- rived through reinforcement learning on past examples. rental demand (kaggle.com/c/bike-sharing-demand) in Fig- ure 2(a). Deriving several features (Figure 2(b)) dramatically Introduction reduces the modeling error. For instance, extracting the hour of the day from the given timestamp feature helps to capture Predictive analytics are widely used in support for decision certain trends such as peak versus non-peak demand. Note making across a variety of domains including fraud detec- that certain valuable features are derived through a compo- tion, marketing, drug discovery, advertising, risk manage- sition of multiple simpler functions.
    [Show full text]
  • Explicit Document Modeling Through Weighted Multiple-Instance Learning
    Journal of Artificial Intelligence Research 58 (2017) Submitted 6/16; published 2/17 Explicit Document Modeling through Weighted Multiple-Instance Learning Nikolaos Pappas [email protected] Andrei Popescu-Belis [email protected] Idiap Research Institute, Rue Marconi 19 CH-1920 Martigny, Switzerland Abstract Representing documents is a crucial component in many NLP tasks, for instance pre- dicting aspect ratings in reviews. Previous methods for this task treat documents globally, and do not acknowledge that target categories are often assigned by their authors with gen- erally no indication of the specific sentences that motivate them. To address this issue, we adopt a weakly supervised learning model, which jointly learns to focus on relevant parts of a document according to the context along with a classifier for the target categories. Originated from the weighted multiple-instance regression (MIR) framework, the model learns decomposable document vectors for each individual category and thus overcomes the representational bottleneck in previous methods due to a fixed-length document vec- tor. During prediction, the estimated relevance or saliency weights explicitly capture the contribution of each sentence to the predicted rating, thus offering an explanation of the rating. Our model achieves state-of-the-art performance on multi-aspect sentiment analy- sis, improving over several baselines. Moreover, the predicted saliency weights are close to human estimates obtained by crowdsourcing, and increase the performance of lexical and topical features for review segmentation and summarization. 1. Introduction Many NLP tasks such as document classification, question answering, and summarization heavily rely on how well the contents of the document are represented in a given model.
    [Show full text]
  • Miforests: Multiple-Instance Learning with Randomized Trees⋆
    MIForests: Multiple-Instance Learning with Randomized Trees? Christian Leistner, Amir Saffari, and Horst Bischof Institute for Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, 8010 Graz, Austria {leistner,saffari,bischof}@icg.tugraz.at http://www.icg.tugraz.at Abstract. Multiple-instance learning (MIL) allows for training classi- fiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many applications such as object clas- sification, detection and tracking. This paper presents a novel multiple- instance learning algorithm for randomized trees called MIForests. Ran- domized trees are fast, inherently parallel and multi-class and are thus increasingly popular in computer vision. MIForest combine the advan- tages of these classifiers with the flexibility of multiple instance learning. In order to leverage the randomized trees for MIL, we define the hidden class labels inside target bags as random variables. These random vari- ables are optimized by training random forests and using a fast iterative homotopy method for solving the non-convex optimization problem. Ad- ditionally, most previously proposed MIL approaches operate in batch or off-line mode and thus assume access to the entire training set. This limits their applicability in scenarios where the data arrives sequentially and in dynamic environments. We show that MIForests are not limited to off-line problems and present an on-line extension of our approach. In the experiments, we evaluate MIForests on standard visual MIL bench- mark datasets where we achieve state-of-the-art results while being faster than previous approaches and being able to inherently solve multi-class problems.
    [Show full text]
  • A New Modal Autoencoder for Functionally Independent Feature Extraction
    A New Modal Autoencoder for Functionally Independent Feature Extraction Yuzhu Guo Kang Pan Simeng Li Zongchang Han Kexin Wang Department of Automation Sciences and Electrical Engineering Beihang University Beijing, China {yuzhuguo, lkwxl, lsimeng, zchan, wkx933}@buaa.edu.cn Li Li Department of Automation Sciences and Electrical Engineering Beihang University Beijing, China [email protected] Abstract Autoencoders have been widely used for dimensional reduction and feature ex- traction. Various types of autoencoders have been proposed by introducing reg- ularization terms. Most of these regularizations improve representation learning by constraining the weights in the encoder part, which maps input into hidden nodes and affects the generation of features. In this study, we show that a con- straint to the decoder can also significantly improve its performance because the decoder determines how the latent variables contribute to the reconstruction of input. Inspired by the structural modal analysis method in mechanical engineering, a new modal autoencoder (MAE) is proposed by othogonalising the columns of the readout weight matrix. The new regularization helps to disentangle explanatory factors of variation and forces the MAE to extract fundamental modes in data. The learned representations are functionally independent in the reconstruction of input and perform better in consecutive classification tasks. The results were validated on the MNIST variations and USPS classification benchmark suite. Comparative experiments clearly show that the new algorithm has a surprising advantage. The new MAE introduces a very simple training principle for autoencoders and could be promising for the pre-training of deep neural networks. arXiv:2006.14390v1 [cs.LG] 25 Jun 2020 1 Introduction The success of machine learning heavily depends on data representation.
    [Show full text]
  • Feature Engineering for Machine Learning
    Feature Engineering for Machine Learning PRINCIPLES AND TECHNIQUES FOR DATA SCIENTISTS Alice Zheng & Amanda Casari Feature Engineering for Machine Learning Principles and Techniques for Data Scientists Alice Zheng and Amanda Casari Beijing Boston Farnham Sebastopol Tokyo Feature Engineering for Machine Learning by Alice Zheng and Amanda Casari Copyright © 2018 Alice Zheng, Amanda Casari. All rights reserved. Printed in the United States of America. Published by O’Reilly Media, Inc., 1005 Gravenstein Highway North, Sebastopol, CA 95472. O’Reilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com/safari). For more information, contact our corporate/insti‐ tutional sales department: 800-998-9938 or [email protected]. Editors: Rachel Roumeliotis and Jeff Bleiel Indexer: Ellen Troutman Production Editor: Kristen Brown Interior Designer: David Futato Copyeditor: Rachel Head Cover Designer: Karen Montgomery Proofreader: Sonia Saruba Illustrator: Rebecca Demarest April 2018: First Edition Revision History for the First Edition 2018-03-23: First Release See http://oreilly.com/catalog/errata.csp?isbn=9781491953242 for release details. The O’Reilly logo is a registered trademark of O’Reilly Media, Inc. Feature Engineering for Machine Learning, the cover image, and related trade dress are trademarks of O’Reilly Media, Inc. While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk.
    [Show full text]
  • A Brief Introduction to Deep Learning
    A Brief Introduction to Deep Learning --Yangyan Li How would you crack it? How to avoid being cracked? Seam Carving! Labradoodle or fried chicken Puppy or bagel Sheepdog or mop Chihuahua or muffin Barn owl or apple Parrot or guacamole Raw chicken or Donald Trump But, we human actually lose! • A demo that shows we, human, lose, on the classification task, we are proud of, we have been trained for millions of years! • If we want to make it hard for bots, it has to be hard for human as well. How would you crack it? We human lose on Go! We (will) lose on many specific tasks! • Speech recognition • Translation • Self-driving • … • BUT, they are not AI yet… • Don’t worry until it dates with your girl/boy friend… Deep learning is so cool for so many problems… A Brief Introduction to Deep Learning • Artificial Neural Network • Back-propagation • Fully Connected Layer • Convolutional Layer • Overfitting Artificial Neural Network 1. Activation function 2. Weights 3. Cost function 4. Learning algorithm Live Demo Neurons are functions Neurons are functions Back-propagation Now, serious stuff, a bit… Fully Connected Layers “When in doubt, use brute force.” --Ken Thompson “If brute force is possible...” --Yangyan Li Convolutional Layers Convolutional Layers Convolution Filters Feature Engineering vs. Learning • Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work. • “When working on a machine learning problem, feature engineering is manually designing what the input x's should be.” -- Shayne Miel • “Coming up with features is difficult, time- consuming, requires expert knowledge.” --Andrew Ng How to detect it in training process? Dropout Sigmod ReLU Sigmod ReLU Compute, connect, evaluate, correct, train madly… Non-linearity, distributed representation, parallel computation, adaptive, self-organizing… A brief history • McCulloch, Warren S., and Walter Pitts.
    [Show full text]
  • A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction
    entropy Article A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction Dingming Wu, Xiaolong Wang * and Shaocong Wu College of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China; [email protected] (D.W.); [email protected] (S.W.) * Correspondence: [email protected] Abstract: The trend prediction of the stock is a main challenge. Accidental factors often lead to short-term sharp fluctuations in stock markets, deviating from the original normal trend. The short- term fluctuation of stock price has high noise, which is not conducive to the prediction of stock trends. Therefore, we used discrete wavelet transform (DWT)-based denoising to denoise stock data. Denoising the stock data assisted us to eliminate the influences of short-term random events on the continuous trend of the stock. The denoised data showed more stable trend characteris- tics and smoothness. Extreme learning machine (ELM) is one of the effective training algorithms for fully connected single-hidden-layer feedforward neural networks (SLFNs), which possesses the advantages of fast convergence, unique results, and it does not converge to a local minimum. Therefore, this paper proposed a combination of ELM- and DWT-based denoising to predict the trend of stocks. The proposed method was used to predict the trend of 400 stocks in China. The predic- tion results of the proposed method are a good proof of the efficacy of DWT-based denoising for stock trends, and showed an excellent performance compared to 12 machine learning algorithms (e.g., recurrent neural network (RNN) and long short-term memory (LSTM)).
    [Show full text]
  • Multiple Instance Learning for ECG Risk Stratification
    Proceedings of Machine Learning Research 85:1{15, 2018 Machine Learning for Healthcare Multiple Instance Learning for ECG Risk Stratification Divya Shanmugam [email protected] Massachusetts Institute of Technology Cambridge, MA, USA Davis Blalock [email protected] Massachusetts Institute of Technology Cambridge, MA, USA John Guttag [email protected] Massachusetts Institute of Technology Cambridge, MA, USA Abstract Patients who suffer an acute coronary syndrome are at elevated risk for adverse cardiovas- cular events such as myocardial infarction and cardiovascular death. Accurate assessment of this risk is crucial to their course of care. We focus on estimating a patient's risk of cardiovascular death after an acute coronary syndrome based on a patient's raw electrocar- diogram (ECG) signal. Learning from this signal is challenging for two reasons: 1) positive examples signifying a downstream cardiovascular event are scarce, causing drastic class imbalance, and 2) each patient's ECG signal consists of thousands of heartbeats, accompa- nied by a single label for the downstream outcome. Machine learning has been previously applied to this task, but most approaches rely on hand-crafted features and domain knowl- edge. We propose a method that learns a representation from the raw ECG signal by using a multiple instance learning framework. We present a learned risk score for cardiovascular death that outperforms existing risk metrics in predicting cardiovascular death within 30, 60, 90, and 365 days on a dataset of 5000 patients. 1. Introduction Machine learning has led to improved risk stratification models for a number of outcomes, including stroke (Li et al., 2016), cancer (Heidari et al., 2018), in-hospital mortality (Gong et al., 2017), and treatment resistance (Perlis, 2013).
    [Show full text]