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Discrete Artificial Bee Colony Optimization Algorithm for Financial Classification Problems

Discrete Artificial Bee Colony Optimization Algorithm for Financial Classification Problems

Yannis Marinakis, Magdalene Marinaki, Nikolaos Matsatsinis, Constantin Zopounidis
Copyright: © 2011 |Volume: 2 |Issue: 1 |Pages: 17
ISSN: 1947-8283|EISSN: 1947-8291|EISBN13: 9781613505670|DOI: 10.4018/jamc.2011010101
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MLA

Marinakis, Yannis, et al. "Discrete Artificial Bee Colony Optimization Algorithm for Financial Classification Problems." IJAMC vol.2, no.1 2011: pp.1-17. http://doi.org/10.4018/jamc.2011010101

APA

Marinakis, Y., Marinaki, M., Matsatsinis, N., & Zopounidis, C. (2011). Discrete Artificial Bee Colony Optimization Algorithm for Financial Classification Problems. International Journal of Applied Metaheuristic Computing (IJAMC), 2(1), 1-17. http://doi.org/10.4018/jamc.2011010101

Chicago

Marinakis, Yannis, et al. "Discrete Artificial Bee Colony Optimization Algorithm for Financial Classification Problems," International Journal of Applied Metaheuristic Computing (IJAMC) 2, no.1: 1-17. http://doi.org/10.4018/jamc.2011010101

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Abstract

Nature-inspired methods are used in various fields for solving a number of problems. This study uses a nature-inspired method, artificial bee colony optimization that is based on the foraging behaviour of bees, for a financial classification problem. Financial decisions are often based on classification models, which are used to assign a set of observations into predefined groups. One important step toward the development of accurate financial classification models involves the selection of the appropriate independent variables (features) that are relevant to the problem. The proposed method uses a discrete version of the artificial bee colony algorithm for the feature selection step while nearest neighbour based classifiers are used for the classification step. The performance of the method is tested using various benchmark datasets from UCI Machine Learning Repository and in a financial classification task involving credit risk assessment. Its results are compared with the results of other nature-inspired methods.

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