Semantic Preserving Data Reduction using Artificial Immune Systems

Abstract

Artificial Immune Systems (AIS) can be defined as soft computing systems inspired by immune system of vertebrates. Immune system is an adaptive pattern recognition system. AIS have been used in pattern recognition, machine learning, optimization and clustering. Feature reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encountered in many areas such as machine learning, pattern recognition and semantic based image retrieval. Unlike other dimensionality reduction methods, feature selectors preserve the original meaning of the features after reduction. In this paper we introduce the capability of AIS for semantic preserving data reduction (SPDR). For this purpose a complete survey is done on artificial immune systems. Then a case study is selected to represent the capability of semantic preserving data reduction of AIS. Experimental results subjectively show and verify the proposed idea.