Classifying heterogenous data artificial immune system

Abstract

Artificial Immune System (AIS) is an emerging technique for the classification task and proved to be a reliable technique. In the previous research, many classifiers including AIS classifiers require the data to be in numerical or categorical data types prior to processing. The transformation of data into any other specific types from their original Ibrm can degrade the originality of the data and consume more space and pre processing time. This paper introduces AIS model using clonal selection technique for classifying heterogeneous data in its original types. The model is able to process the data with the types as represented in the database and it solves some problems highlighted in the AIS reviews. To ensure the consistent conditions and fair comparison, the selected algorithms uses the same set of data as used in the proposed model. Experimental results show that this model produces a better accuracy rate than other immune algorithm and comparable to the standard classifiers on most of the benchmark data from UCI Machine Learning Repository.