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Extreme Learning Machine (ELM) is a popular machine learning method which can flexibly simulate the relationships of real-world classification applications. When facing problems ( i.e. , data sets) with a smaller number of samples ( i.e. , instances), ELM may often result in the overfitting trouble. In this paper, we propose a new Instance Cloned Extreme Learning Machine (IC-ELM for short) which can handle numerous different classification problems. IC-ELM uses an instance cloning method to balance the input data’s distribution and extend the training data set, which alleviates the overfitting issue and enhances the testing classification accuracy. Experiments and comparisons on 20 UCI data sets, and validations on image and text classification applications, demonstrate that IC-ELM is able to achieve superior results compared to the original ELM algorithm and its variants, as well as several other classical machine learning algorithms. [ABSTRACT FROM AUTHOR]

The paper presents an algorithm of decision rules redefinition that is based on evaluation of the importance of elementary conditions occurring in induced rules. Standard and simplified (heuristic) indices of elementary condition importance evaluation are described. There is a comparison of the results obtained by both indices concerning classifiers quality and elementary condition rankings estimated by the indices. The efficiency of the proposed algorithm has been verified on 21 benchmark data sets. Moreover, an analysis of practical applications of the proposed methods for biomedical and medical data analysis is presented. The obtained results show that the redefinition reduces considerably a rule set needed to describe each decision class. Additionally, after the rule set redefinition negated elementary conditions may also occur in new rules. [ABSTRACT FROM AUTHOR]