Two layered Genetic Programming for mixed-attribute data classification

Abstract

The important problem of data classification spans numerous real life applications. The classification problem has been tackled by using Genetic Programming in many successful ways. Most approaches focus on classification of only one type of data. However, most of the real-world data contain a mixture of categorical and continuous attributes. In this paper, we present an approach to classify mixed attribute data using Two Layered Genetic Programming (L2GP). The presented approach does not transform data into any other type and combines the properties of arithmetic expressions (using numerical data) and logical expressions (using categorical data). The outer layer contains logical functions and some nodes. These nodes contain the inner layer and are either logical or arithmetic expressions. Logical expressions give their Boolean output to the outer tree. The arithmetic expressions give a real value as their output. Positive real value is considered true and a negative value is considered false. These outputs of inner layers are used to evaluate the outer layer which determines the classification decision. The proposed classification technique has been applied on various heterogeneous data classification problems and found successful.