Table Of ContentBehavior Analysis with
Machine Learning
Using R
Chapman & Hall/CRC
The R Series
Series Editors
John M. Chambers, Department of Statistics, Stanford University, California, USA
Torsten Hothorn, Division of Biostatistics, University of Zurich, Switzerland
Duncan Temple Lang, Department of Statistics, University of California, Davis, USA
Hadley Wickham, RStudio, Boston, Massachusetts, USA
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Enrique Garcia Ceja
For more information about this series, please visit: https://www.crcpress.com/
Chapman--HallCRC-The-R-Series/book-series/CRCTHERSER
Behavior Analysis with
Machine Learning
Using R
Enrique Garcia Ceja
First edition published 2022
by CRC Press
6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742
and by CRC Press
2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN
© 2022 Enrique Garcia Ceja
CRC Press is an imprint of Taylor & Francis Group, LLC
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Library of Congress Cataloging-in-Publication Data
Names: Garcia Ceja, Enrique, author.
Title: Behavior analysis with machine learning using R / Enrique Garcia
Ceja.
Description: First edition. | London ; Boca Raton : CRC Press, 2022. |
Includes bibliographical references and index. | Summary: “Behavior
Analysis with Machine Learning Using R introduces machine learning and
deep learning concepts and algorithms applied to a diverse set of
behavior analysis problems. It focuses on the practical aspects of
solving such problems based on data collected from sensors or stored in
electronic records. The included examples demonstrate how to perform
common data analysis tasks such as: data exploration, visualization,
preprocessing, data representation, model training and evaluation. All
of this, using the R programming language and real-life behavioral data.
Even though the examples focus on behavior analysis tasks, the covered
underlying concepts and methods can be applied in any other domain. No
prior knowledge in machine learning is assumed. Basic experience with R
and basic knowledge in statistics and high school level mathematics are
beneficial. Features: Build supervised machine learning models to
predict indoor locations based on WiFi signals, recognize physical
activities from smartphone sensors and 3D skeleton data, detect hand
gestures from accelerometer signals, and so on. Program your own
ensemble learning methods and use Multi-View Stacking to fuse signals
from heterogeneous data sources. Use unsupervised learning algorithms to
discover criminal behavioral patterns. Build deep learning neural
networks with TensorFlow and Keras to classify muscle activity from
electromyography signals and Convolutional Neural Networks to detect
smiles in images. Evaluate the performance of your models in traditional
and multi-user settings. Build anomaly detection models such as
Isolation Forests and autoencoders to detect abnormal fish behaviors.
This book is intended for undergraduate/graduate students and
researchers from ubiquitous computing, behavioral ecology, psychology,
e-health, and other disciplines who want to learn the basics of machine
learning and deep learning and for the more experienced individuals who
want to apply machine learning to analyze behavioral data”-- Provided by
publisher.
Identifiers: LCCN 2021028230 (print) | LCCN 2021028231 (ebook) | ISBN
9781032067049 (hardback) | ISBN 9781032067056 (paperback) | ISBN
9781003203469 (ebook)
Subjects: LCSH: Behavioral assessment--Data processing. | Task
analysis--Data processing. | Machine learning. | R (Computer program
language)
Classification: LCC BF176.2 .G37 2022 (print) | LCC BF176.2 (ebook) | DDC
155.2/8--dc23/eng/20211006
LC record available at https://lccn.loc.gov/2021028230
LC ebook record available at https://lccn.loc.gov/2021028231
ISBN: 978-1-032-06704-9 (hbk)
ISBN: 978-1-032-06705-6 (pbk)
ISBN: 978-1-003-20346-9 (ebk)
DOI: 10.1201/9781003203469
Publisher’s note: This book has been prepared from camera-ready copy provided by the authors.
Typeset in LM Roman
by KnowledgeWorks Global Ltd.
To My Family,
who have put up with me despite my bad behavior.
Contents
List of Figures xv
Welcome xxvii
Preface xxxi
1 Introduction to Behavior and Machine Learning 1
1.1 What Is Behavior? . . . . . . . . . . . . . . . . . . . . . 1
1.2 What Is Machine Learning? . . . . . . . . . . . . . . . . 5
1.3 Types of Machine Learning . . . . . . . . . . . . . . . . 7
1.4 Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.4.1 Tables . . . . . . . . . . . . . . . . . . . . . . . . 10
1.4.2 Variable Types . . . . . . . . . . . . . . . . . . . 11
1.4.3 Predictive Models . . . . . . . . . . . . . . . . . 12
1.5 Data Analysis Pipeline . . . . . . . . . . . . . . . . . . . 12
1.6 Evaluating Predictive Models . . . . . . . . . . . . . . . 14
1.7 Simple Classification Example . . . . . . . . . . . . . . . 18
1.7.1 𝑘-fold Cross-validation Example . . . . . . . . . . 27
1.8 Simple Regression Example . . . . . . . . . . . . . . . . 30
1.9 Underfitting and Overfitting . . . . . . . . . . . . . . . . 33
1.10 Bias and Variance. . . . . . . . . . . . . . . . . . . . . . 36
1.11 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . 39
vii
viii Contents
2 Predicting Behavior with Classification Models 41
2.1 k-Nearest Neighbors . . . . . . . . . . . . . . . . . . . . 41
2.1.1 Indoor Location with Wi-Fi Signals . . . . . . . . 43
2.2 Performance Metrics . . . . . . . . . . . . . . . . . . . . 51
2.2.1 Confusion Matrix . . . . . . . . . . . . . . . . . . 53
2.3 Decision Trees . . . . . . . . . . . . . . . . . . . . . . . . 55
2.3.1 Activity Recognition with Smartphones . . . . . 60
2.4 Naive Bayes . . . . . . . . . . . . . . . . . . . . . . . . . 68
2.4.1 Activity Recognition with Naive Bayes . . . . . . 72
2.5 Dynamic Time Warping . . . . . . . . . . . . . . . . . . 80
2.5.1 Hand Gesture Recognition . . . . . . . . . . . . . 89
2.6 Dummy Models . . . . . . . . . . . . . . . . . . . . . . . 96
2.6.1 Most-frequent-class Classifier . . . . . . . . . . . 97
2.6.2 Uniform Classifier . . . . . . . . . . . . . . . . . 100
2.6.3 Frequency-based Classifier . . . . . . . . . . . . . 101
2.6.4 Other Dummy Classifiers . . . . . . . . . . . . . 101
2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . 103
3 Predicting Behavior with Ensemble Learning 105
3.1 Bagging . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
3.1.1 Activity Recognition with Bagging . . . . . . . . 106
3.2 Random Forest . . . . . . . . . . . . . . . . . . . . . . . 112
3.3 Stacked Generalization . . . . . . . . . . . . . . . . . . . 115
3.4 Multi-view Stacking for Home Tasks Recognition . . . . 117
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . 124
4 Exploring and Visualizing Behavioral Data 127
4.1 Talking with Field Experts . . . . . . . . . . . . . . . . . 127
Contents ix
4.2 Summary Statistics . . . . . . . . . . . . . . . . . . . . . 128
4.3 Class Distributions . . . . . . . . . . . . . . . . . . . . . 130
4.4 User-class Sparsity Matrix . . . . . . . . . . . . . . . . . 131
4.5 Boxplots . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
4.6 Correlation Plots . . . . . . . . . . . . . . . . . . . . . . 134
4.6.1 Interactive Correlation Plots . . . . . . . . . . . . 137
4.7 Timeseries . . . . . . . . . . . . . . . . . . . . . . . . . . 138
4.7.1 Interactive Timeseries . . . . . . . . . . . . . . . 140
4.8 Multidimensional Scaling (MDS) . . . . . . . . . . . . . 142
4.9 Heatmaps . . . . . . . . . . . . . . . . . . . . . . . . . . 148
4.10 Automated EDA . . . . . . . . . . . . . . . . . . . . . . 151
4.11 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . 154
5 Preprocessing Behavioral Data 157
5.1 Missing Values . . . . . . . . . . . . . . . . . . . . . . . 158
5.1.1 Imputation . . . . . . . . . . . . . . . . . . . . . 162
5.2 Smoothing . . . . . . . . . . . . . . . . . . . . . . . . . . 164
5.3 Normalization . . . . . . . . . . . . . . . . . . . . . . . . 168
5.4 Imbalanced Classes . . . . . . . . . . . . . . . . . . . . . 172
5.4.1 Random Oversampling . . . . . . . . . . . . . . . 174
5.4.2 SMOTE . . . . . . . . . . . . . . . . . . . . . . . 176
5.5 Information Injection . . . . . . . . . . . . . . . . . . . . 179
5.6 One-hot Encoding . . . . . . . . . . . . . . . . . . . . . 181
5.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . 186
6 Discovering Behaviors with Unsupervised Learning 189
6.1 𝑘-means Clustering . . . . . . . . . . . . . . . . . . . . . 189
6.1.1 Grouping Student Responses . . . . . . . . . . . 192
