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Since extreme learning machine (ELM) was proposed, hundreds of studies have been conducted on this subject in various areas, from theoretical researches to practical applications. However, there are very few papers in the literature to reveal the reasons why in ELM classification the class with the highest output value is being chosen as the predicted class for a given input. In order to give a clear insight into this question, this paper analyzes the rationality of ELM reasoning from the perspective of its inductive bias. The analysis results show that the choice of highest output in ELM is reasonable for both binary and multiclass classification problems. In addition, to deal with multiclass problems ELM uses the well-known one-against-all strategy, in which unclassifiable regions may exist. This paper also gives a clear explanation on how ELM resolves the unclassifiable regions, through both analysis and experiments.
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Huang GB, Zhu QY, Siew CK. Extreme learning machine: theory and applications. Neurocomputing. 2006;70:489–501. CrossRef
Huang G-B, Chen L. Convex incremental extreme learning machine. Neurocomputing. 2007;70:3056–62. CrossRef
Liu N, Wang H. Ensemble based extreme learning machine. IEEE Signal Process Lett. 2010;17:754–7. CrossRef
Zong WW, Huang GB, Chen YQ. Weighted extreme learning machine for imbalance learning. Neurocomputing. 2013;101:229–42. CrossRef
Gao H, Shiji S, Gupta JND, Cheng W. Semi-supervised and unsupervised extreme learning machines. IEEE Trans Cybern. 2014;44:2405–17. CrossRef
Zhang F, Qi L, Chen E. Extended extreme learning machine for biometric signal classification. J Comput Theor Nanosci. 2015;12:1247–51. CrossRef
Liu H, Tian H-Q, Li Y-F. Four wind speed multi-step forecasting models using extreme learning machines and signal decomposing algorithms. Energy Convers Manage. 2015;100:16–22. CrossRef
Li W, Chen C, Su H, Du Q. Local binary patterns and extreme learning machine for hyperspectral imagery classification. IEEE Trans Geosci Remote Sens. 2015;53:3681–93. CrossRef
Vong CM, Tai KI, Pun CM, Wong PK. Fast and accurate face detection by sparse Bayesian extreme learning machine. Neural Comput Appl. 2015;26:1149–56. CrossRef
Huang GB, Zhou HM, Ding XJ, Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern Part B Cybern. 2012;42:513–29. CrossRef
Zhang T. Statistical analysis of some multi-category large margin classification methods. J Mach Learn Res. 2004;5:1225–51.
Abe S. Support vector machines for pattern classification. London: Springer; 2010. CrossRef
Mitchell TM. The need for biases in learning generalizations. New Jersey: Department of Computer Science, Laboratory for Computer Science Research, Rutgers University; 1980.
Mitchell TM. Machine Learning. Burr Ridge: McGraw Hill; 1997.
- An Analytical Study on Reasoning of Extreme Learning Machine for Classification from Its Inductive Bias
Pak Kin Wong
Xiang Hui Gao
Ka In Wong
Chi Man Vong
- Springer US
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