ICMLC 2014: Machine Learning and Cybernetics pp 43-51 | Cite as
Classification Based on Lower Integral and Extreme Learning Machine
Abstract
It is known that the non-linear integral has been generally used as an aggregation operator in classification problems, because it represents the potential interaction of a group of attributes. The lower integral is a type of non-linear integral with respect to non-additive set functions, which represents the minimum potential of efficiency for a group of attributes with interaction. Through solving a linear programming problem, the value of lower integral could be calculated. When we consider the lower integral as a classifier, the difficult step is the learning of the non-additive set function, which is used in lower integral. Then, the Extreme Learning Machine technique is applied to solve the problem and the ELM lower integral classifier is proposed in this paper. The implementations and performances of ELM lower integral classifier and single lower integral classifier are compared by experiments with six data sets.
Keywords
Non-linear integral Lower integral Extreme Learning Machine Possibility distributionPreview
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