A Logarithmic Distance-Based Multi-Objective Genetic Programming Approach for Classification of Imbalanced Data

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{kumar:2022:AC,

• author = "Arvind Kumar and Shivani Goel and Nishant Sinha and Arpit Bhardwaj",
• title = "A Logarithmic Distance-Based Multi-Objective Genetic Programming Approach for Classification of Imbalanced Data",
• booktitle = "Advanced Computing",
• year = "2021",
• editor = "Deepak Garg and Sarangapani Jagannathan and Ankur Gupta and Lalit Garg and Suneet Gupta",
• volume = "1528",
• series = "Communications in Computer and Information Science",
• pages = "294--304",
• address = "Malta",
• month = dec # " 18-19",
• publisher = "Springer",
• note = "Revised Selected Papers",
• keywords = "genetic algorithms, genetic programming, Imbalanced data classification, Fitness function, Multi-objective optimization, Pareto front",
• isbn13 = "978-3-030-95502-1",
• URL = "http://link.springer.com/chapter/10.1007/978-3-030-95502-1_23",
• DOI = "doi:10.1007/978-3-030-95502-1_23",
• abstract = "Standard classification algorithms give biased results when data sets are imbalanced. Genetic Programming, a machine learning algorithm based on the evolution of species in nature, also suffers from the same issue. In this research work, we introduced a logarithmic distance-based multi-objective genetic programming (MOGP) approach for classifying imbalanced data. The proposed approach uses the logarithmic value of the distance between predicted and expected values. This logarithmic value for the minority and the majority classes is treated as two separate objectives while learning. In the final generation, the proposed approach generated a Pareto-front of classifiers with a balanced surface representing the majority and the minority class accuracies for binary classification. The primary advantage of the MOGP technique is that it can produce a set of good-performing classifiers in a single experimental execution. Against the MOGP approach, the canonical GP method requires multiple experimental runs and a priori objective-based fitness function. Another benefit of MOGP is that it explicitly includes the learning bias into the algorithms. For evaluation of the proposed approach, we performed extensive experimentation of five imbalanced problems. The proposed approach results have proven its superiority over the traditional method, where the minority and majority class accuracies are taken as two separate objectives.",
• notes = "Also known as \cite{kumar2022log}",

}

Genetic Programming entries for Arvind Kumar Shivani Goel Nishant Sinha Arpit Bhardwaj

Citations