Integrating Data Selection and Extreme Learning Machine for Imbalanced Data
Year: 2015
Type: conference-paper
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.
Source: Procedia Computer Science
Authors: Umi Mahdiyah, M. Isa Irawan, Elly Matul Imah
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W1200316632