Dynamic Feature Scaling for K-Nearest Neighbor Algorithm
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Life Expectancy and Mortality by Race Using Random Subspace Method
International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-4S2 March, 2019 Life Expectancy and Mortality by Race Using Random Subspace Method Yong Gyu Jung, Dong Kyu Nam and Hee Wan Kim Abstract: In recent years, massive data is pouring out in the In ensemble learning one tries to combine the models information society. As collecting and processing of such vast produced by several learners into an ensemble that performs amount of data is becoming an important issue, it is widely used better than the original learners. One way of combining with various fields of data mining techniques for extracting learners is bootstrap aggregating or bagging, which shows information based on data. In this paper, we analyze the causes of the difference between the expected life expectancy and the each learner a randomly sampled subset of the training number of deaths by using the data of the expected life points so that the learners will produce different models that expectancy and the number of the deaths. To analyze the data can be sensibly averaged. In bagging, one samples training effectively, we will use the REP-tree for a small and simple size points with replacement from the full training set. problem using the Random subspace method, which is composed The random subspace method is similar to bagging except of random subspaces. The performance of the REP-tree that the features are randomly sampled, with replacement, algorithm was analyzed and evaluated for statistical data. Index Terms: Random subspace, REP-tree, racism, life for each learner. -
Data Science (ML-DL-Ai)
Data science (ML-DL-ai) Statistics Multiple Regression Model Building and Evaluation Introduction to Statistics Model post fitting for Inference Types of Statistics Examining Residuals Analytics Methodology and Problem- Regression Assumptions Solving Framework Identifying Influential Observations Populations and samples Detecting Collinearity Parameter and Statistics Uses of variable: Dependent and Categorical Data Analysis Independent variable Describing categorical Data Types of Variable: Continuous and One-way frequency tables categorical variable Association Cross Tabulation Tables Descriptive Statistics Test of Association Probability Theory and Distributions Logistic Regression Model Building Picturing your Data Multiple Logistic Regression and Histogram Interpretation Normal Distribution Skewness, Kurtosis Model Building and scoring for Outlier detection Prediction Introduction to predictive modelling Inferential Statistics Building predictive model Scoring Predictive Model Hypothesis Testing Introduction to Machine Learning and Analytics Analysis of variance (ANOVA) Two sample t-Test Introduction to Machine Learning F-test What is Machine Learning? One-way ANOVA Fundamental of Machine Learning ANOVA hypothesis Key Concepts and an example of ML ANOVA Model Supervised Learning Two-way ANOVA Unsupervised Learning Regression Linear Regression with one variable Exploratory data analysis Model Representation Hypothesis testing for correlation Cost Function Outliers, Types of Relationship, -
Machine Learning V1.1
An Introduction to Machine Learning v1.1 E. J. Sagra Agenda ● Why is Machine Learning in the News again? ● ArtificiaI Intelligence vs Machine Learning vs Deep Learning ● Artificial Intelligence ● Machine Learning & Data Science ● Machine Learning ● Data ● Machine Learning - By The Steps ● Tasks that Machine Learning solves ○ Classification ○ Cluster Analysis ○ Regression ○ Ranking ○ Generation Agenda (cont...) ● Model Training ○ Supervised Learning ○ Unsupervised Learning ○ Reinforcement Learning ● Reinforcement Learning - Going Deeper ○ Simple Example ○ The Bellman Equation ○ Deterministic vs. Non-Deterministic Search ○ Markov Decision Process (MDP) ○ Living Penalty ● Machine Learning - Decision Trees ● Machine Learning - Augmented Random Search (ARS) Why is Machine Learning In The News Again? Processing capabilities General ● GPU’s etc have reached level where Machine ● Tools / Languages / Automation Learning / Deep Learning practical ● Need for Data Science no longer limited to ● Cloud computing allows even individuals the tech giants capability to create / train complex models on ● Education is behind in creating Data vast data sets Scientists ● Organizing data is hard. Organizations Memory (Hard Drive (now SSD) as well RAM) challenged ● Speed / capacity increasing ● High demand due to lack of qualified talent ● Cost decreasing Data ● Volume of Data ● Access to vast public data sets ArtificiaI Intelligence vs Machine Learning vs Deep Learning Artificial Intelligence is the all-encompassing concept that initially erupted Followed by Machine Learning that thrived later Finally Deep Learning is escalating the advances of Artificial Intelligence to another level Artificial Intelligence Artificial intelligence (AI) is perhaps the most vaguely understood field of data science. The main idea behind building AI is to use pattern recognition and machine learning to build an agent able to think and reason as humans do (or approach this ability). -
Dynamic Feature Scaling for Online Learning of Binary Classifiers
Dynamic Feature Scaling for Online Learning of Binary Classifiers Danushka Bollegala University of Liverpool, Liverpool, United Kingdom May 30, 2017 Abstract Scaling feature values is an important step in numerous machine learning tasks. Different features can have different value ranges and some form of a feature scal- ing is often required in order to learn an accurate classifier. However, feature scaling is conducted as a preprocessing task prior to learning. This is problematic in an online setting because of two reasons. First, it might not be possible to accu- rately determine the value range of a feature at the initial stages of learning when we have observed only a handful of training instances. Second, the distribution of data can change over time, which render obsolete any feature scaling that we perform in a pre-processing step. We propose a simple but an effective method to dynamically scale features at train time, thereby quickly adapting to any changes in the data stream. We compare the proposed dynamic feature scaling method against more complex methods for estimating scaling parameters using several benchmark datasets for classification. Our proposed feature scaling method consistently out- performs more complex methods on all of the benchmark datasets and improves classification accuracy of a state-of-the-art online classification algorithm. 1 Introduction Machine learning algorithms require train and test instances to be represented using a set of features. For example, in supervised document classification [9], a document is often represented as a vector of its words and the value of a feature is set to the num- ber of times the word corresponding to the feature occurs in that document. -
Capacity and Trainability in Recurrent Neural Networks
Published as a conference paper at ICLR 2017 CAPACITY AND TRAINABILITY IN RECURRENT NEURAL NETWORKS Jasmine Collins,∗ Jascha Sohl-Dickstein & David Sussillo Google Brain Google Inc. Mountain View, CA 94043, USA {jlcollins, jaschasd, sussillo}@google.com ABSTRACT Two potential bottlenecks on the expressiveness of recurrent neural networks (RNNs) are their ability to store information about the task in their parameters, and to store information about the input history in their units. We show experimentally that all common RNN architectures achieve nearly the same per-task and per-unit capacity bounds with careful training, for a variety of tasks and stacking depths. They can store an amount of task information which is linear in the number of parameters, and is approximately 5 bits per parameter. They can additionally store approximately one real number from their input history per hidden unit. We further find that for several tasks it is the per-task parameter capacity bound that determines performance. These results suggest that many previous results comparing RNN architectures are driven primarily by differences in training effectiveness, rather than differences in capacity. Supporting this observation, we compare training difficulty for several architectures, and show that vanilla RNNs are far more difficult to train, yet have slightly higher capacity. Finally, we propose two novel RNN architectures, one of which is easier to train than the LSTM or GRU for deeply stacked architectures. 1 INTRODUCTION Research and application of recurrent neural networks (RNNs) have seen explosive growth over the last few years, (Martens & Sutskever, 2011; Graves et al., 2009), and RNNs have become the central component for some very successful model classes and application domains in deep learning (speech recognition (Amodei et al., 2015), seq2seq (Sutskever et al., 2014), neural machine translation (Bahdanau et al., 2014), the DRAW model (Gregor et al., 2015), educational applications (Piech et al., 2015), and scientific discovery (Mante et al., 2013)). -
Training and Testing of a Single-Layer LSTM Network for Near-Future Solar Forecasting
applied sciences Conference Report Training and Testing of a Single-Layer LSTM Network for Near-Future Solar Forecasting Naylani Halpern-Wight 1,2,*,†, Maria Konstantinou 1, Alexandros G. Charalambides 1 and Angèle Reinders 2,3 1 Chemical Engineering Department, Cyprus University of Technology, Lemesos 3036, Cyprus; [email protected] (M.K.); [email protected] (A.G.C.) 2 Energy Technology Group, Department of Mechanical Engineering, Eindhoven University of Technology, 5612 AE Eindhoven, The Netherlands; [email protected] 3 Department of Design, Production and Management, Faculty of Engineering Technology, University of Twente, 7522 NB Enschede, The Netherlands * Correspondence: [email protected] or [email protected] † Current address: Archiepiskopou Kyprianou 30, Limassol 3036, Cyprus. Received: 30 June 2020; Accepted: 31 July 2020; Published: 25 August 2020 Abstract: Increasing integration of renewable energy sources, like solar photovoltaic (PV), necessitates the development of power forecasting tools to predict power fluctuations caused by weather. With trustworthy and accurate solar power forecasting models, grid operators could easily determine when other dispatchable sources of backup power may be needed to account for fluctuations in PV power plants. Additionally, PV customers and designers would feel secure knowing how much energy to expect from their PV systems on an hourly, daily, monthly, or yearly basis. The PROGNOSIS project, based at the Cyprus University of Technology, is developing a tool for intra-hour solar irradiance forecasting. This article presents the design, training, and testing of a single-layer long-short-term-memory (LSTM) artificial neural network for intra-hour power forecasting of a single PV system in Cyprus. -
Training Dnns: Tricks
Training DNNs: Tricks Ju Sun Computer Science & Engineering University of Minnesota, Twin Cities March 5, 2020 1 / 33 Recap: last lecture Training DNNs m 1 X min ` (yi; DNNW (xi)) +Ω( W ) W m i=1 { What methods? Mini-batch stochastic optimization due to large m * SGD (with momentum), Adagrad, RMSprop, Adam * diminishing LR (1/t, exp delay, staircase delay) { Where to start? * Xavier init., Kaiming init., orthogonal init. { When to stop? * early stopping: stop when validation error doesn't improve This lecture: additional tricks/heuristics that improve { convergence speed { task-specific (e.g., classification, regression, generation) performance 2 / 33 Outline Data Normalization Regularization Hyperparameter search, data augmentation Suggested reading 3 / 33 Why scaling matters? : 1 Pm | Consider a ML objective: minw f (w) = m i=1 ` (w xi; yi), e.g., 1 Pm | 2 { Least-squares (LS): minw m i=1 kyi − w xik2 h i 1 Pm | w|xi { Logistic regression: minw − m i=1 yiw xi − log 1 + e 1 Pm | 2 { Shallow NN prediction: minw m i=1 kyi − σ (w xi)k2 1 Pm 0 | Gradient: rwf = m i=1 ` (w xi; yi) xi. { What happens when coordinates (i.e., features) of xi have different orders of magnitude? Partial derivatives have different orders of magnitudes =) slow convergence of vanilla GD (recall why adaptive grad methods) 2 1 Pm 00 | | Hessian: rwf = m i=1 ` (w xi; yi) xixi . | { Suppose the off-diagonal elements of xixi are relatively small (e.g., when features are \independent"). { What happens when coordinates (i.e., features) of xi have different orders 2 of magnitude? Conditioning of rwf is bad, i.e., f is elongated 4 / 33 Fix the scaling: first idea Normalization: make each feature zero-mean and unit variance, i.e., make each row of X = [x1;:::; xm] zero-mean and unit variance, i.e. -
A Random Subspace Based Conic Functions Ensemble Classifier
Turkish Journal of Electrical Engineering & Computer Sciences Turk J Elec Eng & Comp Sci (2020) 28: 2165 – 2182 http://journals.tubitak.gov.tr/elektrik/ © TÜBİTAK Research Article doi:10.3906/elk-1911-89 A random subspace based conic functions ensemble classifier Emre ÇİMEN∗ Computational Intelligence and Optimization Laboratory (CIOL), Department of Industrial Engineering, Eskişehir Technical University, Eskişehir, Turkey Received: 14.11.2019 • Accepted/Published Online: 11.04.2020 • Final Version: 29.07.2020 Abstract: Classifiers overfit when the data dimensionality ratio to the number of samples is high in a dataset. This problem makes a classification model unreliable. When the overfitting problem occurs, one can achieve high accuracy in the training; however, test accuracy occurs significantly less than training accuracy. The random subspace method is a practical approach to overcome the overfitting problem. In random subspace methods, the classification algorithm selects a random subset of the features and trains a classifier function trained with the selected features. The classification algorithm repeats the process multiple times, and eventually obtains an ensemble of classifier functions. Conic functions based classifiers achieve high performance in the literature; however, these classifiers cannot overcome the overfitting problem when it is the case data dimensionality ratio to the number of samples is high. The proposed method fills the gap in the conic functions classifiers related literature. In this study, we combine the random subspace method andanovel conic function based classifier algorithm. We present the computational results by comparing the new approach witha wide range of models in the literature. The proposed method achieves better results than the previous implementations of conic function based classifiers and can compete with the other well-known methods. -
Temporal Convolutional Neural Network for the Classification Of
Article Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series Charlotte Pelletier 1*, Geoffrey I. Webb 1 and François Petitjean 1 1 Faculty of Information Technology, Monash University, Melbourne, VIC, 3800 * Correspondence: [email protected] Academic Editor: name Version February 1, 2019 submitted to Remote Sens.; Typeset by LATEX using class file mdpi.cls Abstract: New remote sensing sensors now acquire high spatial and spectral Satellite Image Time Series (SITS) of the world. These series of images are a key component of classification systems that aim at obtaining up-to-date and accurate land cover maps of the Earth’s surfaces. More specifically, the combination of the temporal, spectral and spatial resolutions of new SITS makes possible to monitor vegetation dynamics. Although traditional classification algorithms, such as Random Forest (RF), have been successfully applied for SITS classification, these algorithms do not make the most of the temporal domain. Conversely, some approaches that take into account the temporal dimension have recently been tested, especially Recurrent Neural Networks (RNNs). This paper proposes an exhaustive study of another deep learning approaches, namely Temporal Convolutional Neural Networks (TempCNNs) where convolutions are applied in the temporal dimension. The goal is to quantitatively and qualitatively evaluate the contribution of TempCNNs for SITS classification. This paper proposes a set of experiments performed on one million time series extracted from 46 Formosat-2 images. The experimental results show that TempCNNs are more accurate than RF and RNNs, that are the current state of the art for SITS classification. We also highlight some differences with results obtained in computer vision, e.g. -
Random Forests
1 RANDOM FORESTS Leo Breiman Statistics Department University of California Berkeley, CA 94720 January 2001 Abstract Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Freund and Schapire[1996]), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression. 2 1. Random Forests 1.1 Introduction Significant improvements in classification accuracy have resulted from growing an ensemble of trees and letting them vote for the most popular class. In order to grow these ensembles, often random vectors are generated that govern the growth of each tree in the ensemble. An early example is bagging (Breiman [1996]), where to grow each tree a random selection (without replacement) is made from the examples in the training set. Another example is random split selection (Dietterich [1998]) where at each node the split is selected at random from among the K best splits. -
Introduction to Machine Learning: Examples of Unsupervised And
In [1]: from __future__ import division, print_function, absolute_import, unicode_literals Introduction to Machine Learning: Examples of Unsupervised and Supervised Machine- Learning Algorithms Version 0.1 Broadly speaking, machine-learning methods constitute a diverse collection of data-driven algorithms designed to classify/characterize/analyze sources in multi-dimensional spaces. The topics and studies that fall under the umbrella of machine learning is growing, and there is no good catch-all definition. The number (and variation) of algorithms is vast, and beyond the scope of these exercises. While we will discuss a few specific algorithms today, more importantly, we will explore the scope of the two general methods: unsupervised learning and supervised learning and introduce the powerful (and dangerous?) Python package scikit- learn (http://scikit-learn.org/stable/). By AA Miller (Jet Propulsion Laboratory, California Institute of Technology.) (c) 2016 California Institute of Technology. Government sponsorship acknowledged. In [2]: import numpy as np from astropy.table import Table import matplotlib.pyplot as plt %matplotlib inline Problem 1) Introduction to scikit-learn At the most basic level, scikit-learn makes machine learning extremely easy within Python. By way of example, here is a short piece of code that builds a complex, non-linear model to classify sources in the Iris data set that we learned about yesterday: from sklearn import datasets from sklearn.ensemble import RandomForestClassifier iris = datasets.load_iris() RFclf = RandomForestClassifier().fit(iris.data, iris.target) Those 4 lines of code have constructed a model that is superior to any system of hard cuts that we could have encoded while looking at the multidimensional space. -
Continuous State-Space Models for Optimal Sepsis Treatment-A Deep Reinforcement Learning Approach
CONTINUOUS STATE-SPACE MODELS FOR OPTIMAL SEPSIS TREATMENT Continuous State-Space Models for Optimal Sepsis Treatment - a Deep Reinforcement Learning Approach Aniruddh Raghu [email protected] Matthieu Komorowski [email protected] Leo Anthony Celi [email protected] Peter Szolovits [email protected] Marzyeh Ghassemi [email protected] Computer Science and Artificial Intelligence Lab, MIT Cambridge, MA Abstract Sepsis is a leading cause of mortality in intensive care units (ICUs) and costs hospitals billions annually. Treating a septic patient is highly challenging, because individual patients respond very differently to medical interventions and there is no universally agreed-upon treatment for sepsis. Understanding more about a patient’s physiological state at a given time could hold the key to ef- fective treatment policies. In this work, we propose a new approach to deduce optimal treatment policies for septic patients by using continuous state-space models and deep reinforcement learn- ing. Learning treatment policies over continuous spaces is important, because we retain more of the patient’s physiological information. Our model is able to learn clinically interpretable treat- ment policies, similar in important aspects to the treatment policies of physicians. Evaluating our algorithm on past ICU patient data, we find that our model could reduce patient mortality in the hospital by up to 3.6% over observed clinical policies, from a baseline mortality of 13.7%. The learned treatment policies could be used to aid intensive care clinicians in medical decision making and improve the likelihood of patient survival. 1. Introduction arXiv:1705.08422v1 [cs.LG] 23 May 2017 Sepsis (severe infections with organ failure) is a dangerous condition that costs hospitals billions of pounds in the UK alone (Vincent et al., 2006), and is a leading cause of patient mortality (Co- hen et al., 2006).