A LAD-based evolutionary solution procedure for binary classification problems

Authors

  • Hwang Ho Kim Ajou University
  • Jin Young Choi Ajou University

DOI:

https://doi.org/10.23055/ijietap.2014.21.6.1263

Keywords:

Binary Classification, Logical Analysis of Data, Genetic Algorithm, Patterns, Chromosome, Crossover, Fitness Function

Abstract

Logical analysis of data (LAD) is a data analysis methodology used to solve the binary classification problem via supervised learning based on optimization, combinatorics, and Boolean functions. The LAD framework consists of the following four steps: data binarization, support set generation, pattern generation, and theory formulation. Patterns that contain the hidden structural information calculated from the binarized training data play the most important roles in the theory model, which consists of a union of patterns and allows for the classification of new observations. In this work, we develop a parameterized iterative genetic algorithm (PI-GA) to generate a set of patterns with good characteristics in terms of degree (simplicity-wise preference) and coverage (evidential preference) of patterns. The proposed PI-GA can generate simplicity-wise preferred patterns that also have high coverage through population structure. We show the efficiency and accuracy of the proposed pattern generation method through numerical experiments on benchmark machine learning datasets.

Author Biographies

Hwang Ho Kim, Ajou University

Department of Industrial Engineering

Jin Young Choi, Ajou University

Department of Industrial Engineering

Published

2014-09-11

How to Cite

Kim, H. H., & Choi, J. Y. (2014). A LAD-based evolutionary solution procedure for binary classification problems. International Journal of Industrial Engineering: Theory, Applications and Practice, 21(6). https://doi.org/10.23055/ijietap.2014.21.6.1263

Issue

Section

Special Issue: 2013 IJIE Conference at Busan, Korea