Convolutional Recurrent Neural Network and Lightgbm Ensemble Model for 12-Lead ECG Classification
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Arxiv:2006.04059V1 [Cs.LG] 7 Jun 2020
Soft Gradient Boosting Machine Ji Feng1;2, Yi-Xuan Xu1;3, Yuan Jiang3, Zhi-Hua Zhou3 [email protected], fxuyx, jiangy, [email protected] 1Sinovation Ventures AI Institute 2Baiont Technology 3National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China Abstract Gradient Boosting Machine has proven to be one successful function approximator and has been widely used in a variety of areas. However, since the training procedure of each base learner has to take the sequential order, it is infeasible to parallelize the training process among base learners for speed-up. In addition, under online or incremental learning settings, GBMs achieved sub-optimal performance due to the fact that the previously trained base learners can not adapt with the environment once trained. In this work, we propose the soft Gradient Boosting Machine (sGBM) by wiring multiple differentiable base learners together, by injecting both local and global objectives inspired from gradient boosting, all base learners can then be jointly optimized with linear speed-up. When using differentiable soft decision trees as base learner, such device can be regarded as an alternative version of the (hard) gradient boosting decision trees with extra benefits. Experimental results showed that, sGBM enjoys much higher time efficiency with better accuracy, given the same base learner in both on-line and off-line settings. arXiv:2006.04059v1 [cs.LG] 7 Jun 2020 1. Introduction Gradient Boosting Machine (GBM) [Fri01] has proven to be one successful function approximator and has been widely used in a variety of areas [BL07, CC11]. The basic idea is to train a series of base learners that minimize some predefined differentiable loss function in a sequential fashion. -
A Tensor Compiler for Unified Machine Learning Prediction Serving
A Tensor Compiler for Unified Machine Learning Prediction Serving Supun Nakandala, UC San Diego; Karla Saur, Microsoft; Gyeong-In Yu, Seoul National University; Konstantinos Karanasos, Carlo Curino, Markus Weimer, and Matteo Interlandi, Microsoft https://www.usenix.org/conference/osdi20/presentation/nakandala This paper is included in the Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation November 4–6, 2020 978-1-939133-19-9 Open access to the Proceedings of the 14th USENIX Symposium on Operating Systems Design and Implementation is sponsored by USENIX A Tensor Compiler for Unified Machine Learning Prediction Serving Supun Nakandalac,∗, Karla Saurm, Gyeong-In Yus,∗, Konstantinos Karanasosm, Carlo Curinom, Markus Weimerm, Matteo Interlandim mMicrosoft, cUC San Diego, sSeoul National University {<name>.<surname>}@microsoft.com,[email protected], [email protected] Abstract TensorFlow [13] combined, and growing faster than both. Machine Learning (ML) adoption in the enterprise requires Acknowledging this trend, traditional ML capabilities have simpler and more efficient software infrastructure—the be- been recently added to DNN frameworks, such as the ONNX- spoke solutions typical in large web companies are simply ML [4] flavor in ONNX [25] and TensorFlow’s TFX [39]. untenable. Model scoring, the process of obtaining predic- When it comes to owning and operating ML solutions, en- tions from a trained model over new data, is a primary con- terprises differ from early adopters in their focus on long-term tributor to infrastructure complexity and cost as models are costs of ownership and amortized return on investments [68]. trained once but used many times. In this paper we propose As such, enterprises are highly sensitive to: (1) complexity, HUMMINGBIRD, a novel approach to model scoring, which (2) performance, and (3) overall operational efficiency of their compiles featurization operators and traditional ML models software infrastructure [14]. -
Xgboost: a Scalable Tree Boosting System
XGBoost: A Scalable Tree Boosting System Tianqi Chen Carlos Guestrin University of Washington University of Washington [email protected] [email protected] ABSTRACT problems. Besides being used as a stand-alone predictor, it Tree boosting is a highly effective and widely used machine is also incorporated into real-world production pipelines for learning method. In this paper, we describe a scalable end- ad click through rate prediction [15]. Finally, it is the de- to-end tree boosting system called XGBoost, which is used facto choice of ensemble method and is used in challenges widely by data scientists to achieve state-of-the-art results such as the Netflix prize [3]. on many machine learning challenges. We propose a novel In this paper, we describe XGBoost, a scalable machine learning system for tree boosting. The system is available sparsity-aware algorithm for sparse data and weighted quan- 2 tile sketch for approximate tree learning. More importantly, as an open source package . The impact of the system has we provide insights on cache access patterns, data compres- been widely recognized in a number of machine learning and sion and sharding to build a scalable tree boosting system. data mining challenges. Take the challenges hosted by the machine learning competition site Kaggle for example. A- By combining these insights, XGBoost scales beyond billions 3 of examples using far fewer resources than existing systems. mong the 29 challenge winning solutions published at Kag- gle's blog during 2015, 17 solutions used XGBoost. Among these solutions, eight solely used XGBoost to train the mod- Keywords el, while most others combined XGBoost with neural net- Large-scale Machine Learning s in ensembles. -
Lightgbm Release 3.2.1.99
LightGBM Release 3.2.1.99 Microsoft Corporation Sep 26, 2021 CONTENTS: 1 Installation Guide 3 2 Quick Start 21 3 Python-package Introduction 23 4 Features 29 5 Experiments 37 6 Parameters 43 7 Parameters Tuning 65 8 C API 71 9 Python API 99 10 Distributed Learning Guide 175 11 LightGBM GPU Tutorial 185 12 Advanced Topics 189 13 LightGBM FAQ 191 14 Development Guide 199 15 GPU Tuning Guide and Performance Comparison 201 16 GPU SDK Correspondence and Device Targeting Table 205 17 GPU Windows Compilation 209 18 Recommendations When Using gcc 229 19 Documentation 231 20 Indices and Tables 233 Index 235 i ii LightGBM, Release 3.2.1.99 LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages: • Faster training speed and higher efficiency. • Lower memory usage. • Better accuracy. • Support of parallel, distributed, and GPU learning. • Capable of handling large-scale data. For more details, please refer to Features. CONTENTS: 1 LightGBM, Release 3.2.1.99 2 CONTENTS: CHAPTER ONE INSTALLATION GUIDE This is a guide for building the LightGBM Command Line Interface (CLI). If you want to build the Python-package or R-package please refer to Python-package and R-package folders respectively. All instructions below are aimed at compiling the 64-bit version of LightGBM. It is worth compiling the 32-bit version only in very rare special cases involving environmental limitations. The 32-bit version is slow and untested, so use it at your own risk and don’t forget to adjust some of the commands below when installing. -
Downloaded from the Cancer Imaging Archive (TCIA)1 [13]
UC Irvine UC Irvine Electronic Theses and Dissertations Title Deep Learning for Automated Medical Image Analysis Permalink https://escholarship.org/uc/item/78w60726 Author Zhu, Wentao Publication Date 2019 License https://creativecommons.org/licenses/by/4.0/ 4.0 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, IRVINE Deep Learning for Automated Medical Image Analysis DISSERTATION submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in Computer Science by Wentao Zhu Dissertation Committee: Professor Xiaohui Xie, Chair Professor Charless C. Fowlkes Professor Sameer Singh 2019 c 2019 Wentao Zhu DEDICATION To my wife, little son and my parents ii TABLE OF CONTENTS Page LIST OF FIGURES vi LIST OF TABLES x LIST OF ALGORITHMS xi ACKNOWLEDGMENTS xii CURRICULUM VITAE xiii ABSTRACT OF THE DISSERTATION xvi 1 Introduction 1 1.1 Dissertation Outline and Contributions . 3 2 Adversarial Deep Structured Nets for Mass Segmentation from Mammograms 7 2.1 Introduction . 7 2.2 FCN-CRF Network . 9 2.3 Adversarial FCN-CRF Nets . 10 2.4 Experiments . 12 2.5 Conclusion . 16 3 Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification 17 3.1 Introduction . 17 3.2 Deep MIL for Whole Mammogram Mass Classification . 19 3.2.1 Max Pooling-Based Multi-Instance Learning . 20 3.2.2 Label Assignment-Based Multi-Instance Learning . 22 3.2.3 Sparse Multi-Instance Learning . 23 3.3 Experiments . 24 3.4 Conclusion . 27 iii 4 DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification 29 4.1 Introduction . -
2020 Sut20 CASC-J10.Pdf
CASC-J10 CASC-J10 CASC-J10 CASC-J10 Proceedings of the 10th IJCAR ATP System Competition (CASC-J10) Geo↵Sutcli↵e University of Miami, USA Abstract The CADE ATP System Competition (CASC) evaluates the performance of sound, fully automatic, classical logic, ATP systems. The evaluation is in terms of the number of problems solved, the number of acceptable proofs and models produced, and the average runtime for problems solved, in the context of a bounded number of eligible problems chosen from the TPTP problem library and other useful sources of test problems, and specified time limits on solution attempts. The 10th IJCAR ATP System Competition (CASC-J10) was held on 2th July 2020. The design of the competition and its rules, and information regarding the competing systems, are provided in this report. 1 Introduction The CADE and IJCAR conferences are the major forum for the presentation of new research in all aspects of automated deduction. In order to stimulate ATP research and system de- velopment, and to expose ATP systems within and beyond the ATP community, the CADE ATP System Competition (CASC) is held at each CADE and IJCAR conference. CASC-J10 was held on 2nd July 2020, as part of the 10th International Joint Conference on Automated Reasoning (IJCAR 2020)1, Online, Earth. It was the twenty-fifth competition in the CASC series [139, 145, 142, 95, 97, 138, 136, 137, 102, 104, 106, 108, 111, 113, 115, 117, 119, 121, 123, 144, 125, 127, 130, 132]. CASC evaluates the performance of sound, fully automatic, classical logic, ATP systems. -
Gradient Boosting Meets Graph Neural Networks
Published as a conference paper at ICLR 2021 BOOST THEN CONVOLVE:GRADIENT BOOSTING MEETS GRAPH NEURAL NETWORKS Sergei Ivanov Liudmila Prokhorenkova Criteo AI Lab; Skoltech Yandex; HSE University; MIPT Paris, France Moscow, Russia [email protected] [email protected] ABSTRACT Graph neural networks (GNNs) are powerful models that have been successful in various graph representation learning tasks. Whereas gradient boosted decision trees (GBDT) often outperform other machine learning methods when faced with heterogeneous tabular data. But what approach should be used for graphs with tabular node features? Previous GNN models have mostly focused on networks with homogeneous sparse features and, as we show, are suboptimal in the heterogeneous setting. In this work, we propose a novel architecture that trains GBDT and GNN jointly to get the best of both worlds: the GBDT model deals with heterogeneous features, while GNN accounts for the graph structure. Our model benefits from end- to-end optimization by allowing new trees to fit the gradient updates of GNN. With an extensive experimental comparison to the leading GBDT and GNN models, we demonstrate a significant increase in performance on a variety of graphs with tabular features. The code is available: https://github.com/nd7141/bgnn. 1 INTRODUCTION Graph neural networks (GNNs) have shown great success in learning on graph-structured data with various applications in molecular design (Stokes et al., 2020), computer vision (Casas et al., 2019), combinatorial optimization (Mazyavkina et al., 2020), and recommender systems (Sun et al., 2020). The main driving force for progress is the existence of canonical GNN architecture that efficiently encodes the original input data into expressive representations, thereby achieving high-quality results on new datasets and tasks. -
Comparative Performance of Machine Learning Algorithms in Cyberbullying Detection: Using Turkish Language Preprocessing Techniques
Comparative Performance of Machine Learning Algorithms in Cyberbullying Detection: Using Turkish Language Preprocessing Techniques Emre Cihan ATESa*, Erkan BOSTANCIb, Mehmet Serdar GÜZELc a Department of Security Science, Gendarmerie and Coast Guard Academy (JSGA), Ankara, Turkey. b Department of Computer Engineering, Ankara University, Ankara, Turkey. c Department of Computer Engineering, Ankara University, Ankara, Turkey. Abstract With the increasing use of the internet and social media, it is obvious that cyberbullying has become a major problem. The most basic way for protection against the dangerous consequences of cyberbullying is to actively detect and control the contents containing cyberbullying. When we look at today's internet and social media statistics, it is impossible to detect cyberbullying contents only by human power. Effective cyberbullying detection methods are necessary in order to make social media a safe communication space. Current research efforts focus on using machine learning for detecting and eliminating cyberbullying. Although most of the studies have been conducted on English texts for the detection of cyberbullying, there are few studies in Turkish. Limited methods and algorithms were also used in studies conducted on the Turkish language. In addition, the scope and performance of the algorithms used to classify the texts containing cyberbullying is different, and this reveals the importance of using an appropriate algorithm. The aim of this study is to compare the performance of different machine learning algorithms in detecting Turkish messages containing cyberbullying. In this study, nineteen different classification algorithms were used to identify texts containing cyberbullying using Turkish natural language processing techniques. Precision, recall, accuracy and F1 score values were used to evaluate the performance of classifiers. -
Characterization and Prediction of Deep Learning Workloads in Large-Scale GPU Datacenters
Characterization and Prediction of Deep Learning Workloads in Large-Scale GPU Datacenters Qinghao Hu1,2 Peng Sun3 Shengen Yan3 Yonggang Wen1 Tianwei Zhang1∗ 1School of Computer Science and Engineering, Nanyang Technological University 2S-Lab, Nanyang Technological University 3SenseTime {qinghao.hu, ygwen, tianwei.zhang}@ntu.edu.sg {sunpeng1, yanshengen}@sensetime.com ABSTRACT 1 INTRODUCTION Modern GPU datacenters are critical for delivering Deep Learn- Over the years, we have witnessed the remarkable impact of Deep ing (DL) models and services in both the research community and Learning (DL) technology and applications on every aspect of our industry. When operating a datacenter, optimization of resource daily life, e.g., face recognition [65], language translation [64], adver- scheduling and management can bring significant financial bene- tisement recommendation [21], etc. The outstanding performance fits. Achieving this goal requires a deep understanding of thejob of DL models comes from the complex neural network structures features and user behaviors. We present a comprehensive study and may contain trillions of parameters [25]. Training a production about the characteristics of DL jobs and resource management. model may require large amounts of GPU resources to support First, we perform a large-scale analysis of real-world job traces thousands of petaflops operations [15]. Hence, it is a common prac- from SenseTime. We uncover some interesting conclusions from tice for research institutes, AI companies and cloud providers to the perspectives of clusters, jobs and users, which can facilitate the build large-scale GPU clusters to facilitate DL model development. cluster system designs. Second, we introduce a general-purpose These clusters are managed in a multi-tenancy fashion, offering framework, which manages resources based on historical data. -
A Universal Neural Network Solution for Tabular Data
Under review as a conference paper at ICLR 2019 TABNN: A UNIVERSAL NEURAL NETWORK SOLUTION FOR TABULAR DATA Anonymous authors Paper under double-blind review ABSTRACT Neural Networks (NN) have achieved state-of-the-art performance in many tasks within image, speech, and text domains. Such great success is mainly due to special structure design to fit the particular data patterns, such as CNN captur- ing spatial locality and RNN modeling sequential dependency. Essentially, these specific NNs achieve good performance by leveraging the prior knowledge over corresponding domain data. Nevertheless, there are many applications with all kinds of tabular data in other domains. Since there are no shared patterns among these diverse tabular data, it is hard to design specific structures to fit them all. Without careful architecture design based on domain knowledge, it is quite chal- lenging for NN to reach satisfactory performance in these tabular data domains. To fill the gap of NN in tabular data learning, we propose a universal neural net- work solution, called TabNN, to derive effective NN architectures for tabular data in all kinds of tasks automatically. Specifically, the design of TabNN follows two principles: to explicitly leverage expressive feature combinations and to reduce model complexity. Since GBDT has empirically proven its strength in modeling tabular data, we use GBDT to power the implementation of TabNN. Comprehen- sive experimental analysis on a variety of tabular datasets demonstrate that TabNN can achieve much better performance than many baseline solutions. 1 INTRODUCTION Recent years have witnessed the extraordinary success of Neural Networks (NN), especially Deep Neural Networks, in achieving state-of-the-art performances in many domains, such as image clas- sification (He et al., 2016), speech recognition (Graves et al., 2013), and text mining (Goodfellow et al., 2016). -
Enhanced Prediction of Hot Spots at Protein-Protein Interfaces Using Extreme Gradient Boosting
www.nature.com/scientificreports OPEN Enhanced Prediction of Hot Spots at Protein-Protein Interfaces Using Extreme Gradient Boosting Received: 5 June 2018 Hao Wang, Chuyao Liu & Lei Deng Accepted: 10 September 2018 Identifcation of hot spots, a small portion of protein-protein interface residues that contribute the Published: xx xx xxxx majority of the binding free energy, can provide crucial information for understanding the function of proteins and studying their interactions. Based on our previous method (PredHS), we propose a new computational approach, PredHS2, that can further improve the accuracy of predicting hot spots at protein-protein interfaces. Firstly we build a new training dataset of 313 alanine-mutated interface residues extracted from 34 protein complexes. Then we generate a wide variety of 600 sequence, structure, exposure and energy features, together with Euclidean and Voronoi neighborhood properties. To remove redundant and irrelevant information, we select a set of 26 optimal features utilizing a two-step feature selection method, which consist of a minimum Redundancy Maximum Relevance (mRMR) procedure and a sequential forward selection process. Based on the selected 26 features, we use Extreme Gradient Boosting (XGBoost) to build our prediction model. Performance of our PredHS2 approach outperforms other machine learning algorithms and other state-of-the-art hot spot prediction methods on the training dataset and the independent test set (BID) respectively. Several novel features, such as solvent exposure characteristics, second structure features and disorder scores, are found to be more efective in discriminating hot spots. Moreover, the update of the training dataset and the new feature selection and classifcation algorithms play a vital role in improving the prediction quality. -
Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow
water Article Development and Evaluation of the Combined Machine Learning Models for the Prediction of Dam Inflow Jiyeong Hong 1 , Seoro Lee 1, Joo Hyun Bae 2 , Jimin Lee 1, Woon Ji Park 1, Dongjun Lee 1, Jonggun Kim 1 and Kyoung Jae Lim 1,* 1 Department of Regional Infrastructure Engineering, Kangwon National University, Chuncheon-si 24341, Korea; [email protected] (J.H.); [email protected] (S.L.); [email protected] (J.L.); [email protected] (W.J.P.); [email protected] (D.L.); [email protected] (J.K.) 2 Korea Water Environment Research Institute, Chuncheon-si 24408, Korea; [email protected] * Correspondence: [email protected] Received: 3 September 2020; Accepted: 15 October 2020; Published: 20 October 2020 Abstract: Predicting dam inflow is necessary for effective water management. This study created machine learning algorithms to predict the amount of inflow into the Soyang River Dam in South Korea, using weather and dam inflow data for 40 years. A total of six algorithms were used, as follows: decision tree (DT), multilayer perceptron (MLP), random forest (RF), gradient boosting (GB), recurrent neural network–long short-term memory (RNN–LSTM), and convolutional neural network–LSTM (CNN–LSTM). Among these models, the multilayer perceptron model showed the best results in predicting dam inflow, with the Nash–Sutcliffe efficiency (NSE) value of 0.812, root mean squared errors (RMSE) of 77.218 m3/s, mean absolute error (MAE) of 29.034 m3/s, correlation coefficient (R) of 0.924, and determination coefficient (R2) of 0.817. However, when the amount of dam inflow is below 100 m3/s, the ensemble models (random forest and gradient boosting models) performed better than MLP for the prediction of dam inflow.