A Novel Emotion Recognition Method Based on Ensemble Learning and Rough Set Theory

Emotion recognition is a very hot topic, which is related with computer science, psychology, artificial intelligence, etc. It is always performed on facial or audio information with classical method such as ANN, fuzzy set, SVM, HMM, etc. Ensemble learning theory is a novelty in machine learning and ensemble method is proved an effective pattern recognition method. In this paper, a novel ensemble learning method is proposed, which is based on selective ensemble feature selection and rough set theory. This method can meet the tradeoff between accuracy and diversity of base classifiers. Moreover, the proposed method is taken as an emotion recognition method and proved to be effective according to the simulation experiments.

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A Novel Emotion Recognition Method Based on Ensemble Learning and Rough Set Theory

International Journal of Cognitive Informatics and Natural Intelligence ◽

10.4018/ijcini.2011070104 ◽

2011 ◽

Vol 5 (3) ◽

pp. 61-72 ◽

Cited By ~ 2

Author(s):

Yong Yang ◽

Guoyin Wang

Keyword(s):

Emotion Recognition ◽

Ensemble Learning ◽

Set Theory ◽

Rough Set ◽

Rough Set Theory ◽

Classical Method ◽

Pattern Recognition Method ◽

Recognition Method ◽

Simulation Experiments ◽

Audio Information

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A Data Driven Emotion Recognition Method Based on Rough Set Theory

Rough Sets and Current Trends in Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-540-88425-5_47 ◽

2008 ◽

pp. 457-464

Author(s):

Yong Yang ◽

Guoyin Wang ◽

Fei Luo ◽

Zhenjing Li

Keyword(s):

Emotion Recognition ◽

Set Theory ◽

Rough Set ◽

Rough Set Theory ◽

Data Driven ◽

Recognition Method

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Research on Information Technology with Character Pattern Recognition Method Based on Rough Set Theory

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.886.519 ◽

2014 ◽

Vol 886 ◽

pp. 519-523 ◽

Cited By ~ 2

Author(s):

Yong Li Liu

Keyword(s):

Information Technology ◽

Pattern Recognition ◽

Set Theory ◽

Rough Set ◽

Rough Set Theory ◽

Decision Rules ◽

Pattern Recognition Method ◽

Two Dimensional ◽

Recognition Method ◽

Two Dimensional Image

Character Pattern recognition is widely used in the information technology field. This paper proposes a method of character pattern recognition based on rough set theory. By giving the characters two dimensional image, defining the location of the characteristic and abstracting the characteristic value, the knowledge table and table reduction can be ascertained. Then the decision rules can be deduced. Through the simulation of 26 English alphabets, the results illustrate this methods validity and correctness.

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On Objective-Based Roughc-Regression

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2015.p0036 ◽

2015 ◽

Vol 19 (1) ◽

pp. 36-42

Author(s):

Akira Sugawara ◽

◽

Yasunori Endo ◽

Naohiko Kinoshita ◽

Keyword(s):

Pattern Recognition ◽

Set Theory ◽

Rough Set ◽

Fuzzy Set Theory ◽

Clustering Algorithm ◽

Rough Set Theory ◽

Pattern Recognition Method ◽

Clustering Methods ◽

Recognition Method ◽

Numerical Examples

The pattern recognition method of clustering is a technique automatically classifying data into clusters. Among clustering methods,c-regression based on fuzzy set theory, called Fuzzyc-Regression (FCR), is proposed to get a linear dataset structure. The most recent clustering is based on rough set theory called rough clustering, which is less descriptive than fuzzy clustering. A typical rough clustering algorithm is Roughk-Regression (RKR). However, RKR has problems because it depends on initial values and has no optimum index, so we do not know whether a clustering result will be optimal. This paper proposes Roughc-Regression (RCR) based on the optimization of an objective function and demonstrates its effectiveness through numerical examples.

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A novel emotion recognition approach based on ensemble learning and rough set theory

9th IEEE International Conference on Cognitive Informatics (ICCI'10) ◽

10.1109/coginf.2010.5599818 ◽

2010 ◽

Cited By ~ 1

Author(s):

Yong Yang ◽

Guoyin Wang ◽

Zhiyu Zhang ◽

Kan Tian

Keyword(s):

Emotion Recognition ◽

Ensemble Learning ◽

Set Theory ◽

Rough Set ◽

Rough Set Theory

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Important Attributes Selection Based on Rough Set for Speech Emotion Recognition

Transdisciplinary Advancements in Cognitive Mechanisms and Human Information Processing ◽

10.4018/978-1-60960-553-7.ch016 ◽

2011 ◽

pp. 262-271

Author(s):

Jian Zhou ◽

Guoyin Wang ◽

Yong Yang

Keyword(s):

Emotion Recognition ◽

Set Theory ◽

Rough Set ◽

Rough Set Theory ◽

Recognition Rate ◽

Feature Selection Method ◽

Recognition System ◽

Attribute Selection ◽

Computer Application ◽

Speech Emotion Recognition

Speech emotion recognition is becoming more and more important in such computer application fields as health care, children education, etc. In order to improve the prediction performance or providing faster and more cost-effective recognition system, an attribute selection is often carried out beforehand to select the important attributes from the input attribute sets. However, it is time-consuming for traditional feature selection method used in speech emotion recognition to determine an optimum or suboptimum feature subset. Rough set theory offers an alternative, formal and methodology that can be employed to reduce the dimensionality of data. The purpose of this study is to investigate the effectiveness of Rough Set Theory in identifying important features in speech emotion recognition system. The experiments on CLDC emotion speech database clearly show this approach can reduce the calculation cost while retaining a suitable high recognition rate.

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