Name
Abstract | ||
|---|---|---|
Emotion recognition is very important for applications of human-computer intelligent interaction. It is always performed on facial or audio information with such method as ANN, fuzzy set, SVM, HMM, etc. Ensemble learning is a hot topic in machine learning and ensemble method is proved an effective pattern recognition method. In this paper, a novel ensemble learning method which is based on selective ensemble feature selection and rough set theory is proposed, and it meets the tradeoff between the 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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/COGINF.2010.5599818 | IEEE ICCI |
Keywords | Field | DocType |
rough set theory,fuzzy set theory,ensemble learning method,human computer interaction,learning (artificial intelligence),selective ensemble,hmm,emotion recognition,human-computer intelligent interaction,ann,ensemble learning,feature selection,machine learning,selective ensemble feature selection,effective pattern recognition method,emotion recognition approach,rough set,artificial neural networks,learning artificial intelligence,classification algorithms,information systems,pattern recognition,fuzzy set,simulation experiment,computational intelligence,set theory | Feature selection,Pattern recognition,Computer science,Support vector machine,Fuzzy set,Rough set,Artificial intelligence,Artificial neural network,Hidden Markov model,Statistical classification,Ensemble learning,Machine learning | Conference |
Volume | Issue | ISBN |
null | null | 978-1-4244-8041-8 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong Yang | 1 | 54 | 12.22 |
| Guoyin Wang | 2 | 2144 | 202.16 |
| Zhiyu Zhang | 3 | 0 | 2.03 |
| Kan Tian | 4 | 0 | 0.68 |