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A Classification Model using Neuro Fuzzy Classifier for Imbalanced Data

Anazida Zainal, Mohd Aizaini Maarof, Siti Mariyam Shamsuddin


Most of the biological data often deals with class imbalance problem. This happens due to the heterogeneous data and also several categorical attributes. This induces the researchers to work in this area to handle the data imbalance problem. The main challenges faced in bioinformatics are the manner by which to unravel the logical issues as opposed to concentrating too vigorously on gathering and examining biological information. As a result of the unpredictability, there are various testing research issues in bioinformatics. For the most part, information examination related issues in bioinformatics can be separated into three classes as indicated by the sort of biological data: sequences, structures, and networks. Classification and clustering strategies of data mining plays a critical part to dissect biological data such as genomic/DNA microarray data classification and analysis. Learning from imbalanced datasets is a common problem found in many bioinformatics applications, such as gene prediction, splice site prediction, promoter prediction, protein classification and many more. In this work neuro fuzzy model is presented for the data imbalance classification problem.


Big Biological Data, Data Mining, Class Imbalance Problem, Logical Difficulties, Multi Class Labels.

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