Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Toward Machine Emotional Intelligence: Analysis of Affective Physiological State
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Online classification of nonstationary data streams
Intelligent Data Analysis
Classifier ensembles: Select real-world applications
Information Fusion
Estimation and decision fusion: A survey
Neurocomputing
Comparing Two Emotion Models for Deriving Affective States from Physiological Data
Affect and Emotion in Human-Computer Interaction
Emotion Recognition Based on Physiological Changes in Music Listening
IEEE Transactions on Pattern Analysis and Machine Intelligence
An ensemble approach for incremental learning in nonstationary environments
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications
IEEE Transactions on Affective Computing
Combining classifiers in multimodal affect detection
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
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Physiological signals are widely considered to contain affective information. Consequently, pattern recognition techniques such as classification are commonly used to detect affective states from physiological data. Previous studies have achieved some success in detecting affect from physiological measures, especially in controlled environments where emotions are experimentally induced. One challenge that arises is that physiological measures are expected to exhibit considerable day variations due to a number of extraneous factors such as environmental changes and sensor placements. These variations pose challenges to effectively classify affective sates from future physiological data; this is a common problem for real world requirements. The present study provides a quantitative analysis of day variations of physiological signals from different subjects. We propose a classifier ensemble approach using a Winnow algorithm to address the problem of day-variation in physiological signals. Our results show that the Winnow ensemble approach outperformed a static classification approach for detecting affective states from physiological signals that exhibited day variations.