Learning and Intelligent Optimization
Honey Bees Mating Optimization algorithm for financial classification problems
Applied Soft Computing
Journal of Global Optimization
A hybrid particle Swarm optimization algorithm for clustering analysis
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Discrete Artificial Bee Colony Optimization Algorithm for Financial Classification Problems
International Journal of Applied Metaheuristic Computing
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The classification problem consists of using some known objects, usually described by a large vector of features, to induce a model that classifies others into known classes. The present paper deals with the optimization of Nearest Neighbor Classifiers via Metaheuristic Algorithms. The Metaheuristic Algorithms used include tabu search, genetic algorithms and ant colony optimization. The performance of the proposed algorithms is tested using data from 1411 firms derived from the loan portfolio of a leading Greek Commercial Bank in order to classify the firms in different groups representing different levels of credit risk. Also, a comparison of the algorithm with other methods such as UTADIS, SVM, CART, and other classification methods is performed using these data.