Flexible Immune Network Recognition System for Mining Heterogeneous Data

Abstract

Artificial Immune System (AIS) is an emerging technique for the classification task and proved to be a reliable technique. In previous studies, 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 form can degrade the originality of the data and consume more space and pre processing time. This paper introduces AIS model using immune network 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 bias problems highlighted in the AIS review papers. To ensure the consistent conditions and fair comparison, the selected existing algorithms use the same set of data as used in the proposed model. Experimental results show that this network-based model produces a better accuracy rate than the existing population-based immune algorithm and than the standard classifiers on most of the data from University of California, Irvive (UCI) Machine Learning Repository (MLR) and University of California, Riverside (UCR) Time Series Data (TSR).Keywordsartificial immune system (AIS)classificationimmune networkheterogeneousaccuracysignificant difference