Integrating Data Selection and Extreme Learning Machine for Imbalanced Data

Abstract

Abstract Extreme Learning Machine (ELM) is one of the artificial neural network method that introduced by Huang, this method has very fast learning capability. ELM is designed for balance data. Common problems in real-life is imbalanced data problem. So, for imbalanced data problem needs special treatment, because characteristics of the imbalanced data can decrease the accuracy of the data classification. The proposed method in this study is modified ELM to overcome the problems of imbalanced data by integrating the data selection process, which is called by Integrating the data selection and extreme learning machine (IDELM. Performances of learning method are evaluated using 13 imbalanced data from UCI Machine Learning Repository and Benchmark Data Sets for Highly Imbalanced Binary Classification (BDS). The validation includes comparison with some learning algorithms and the result showcases that average perform of our proposed learning method is compete and even outperform of some algorithm in some cases.