A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Ant Colony Optimization
Feature selection in scientific applications
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Developing and Testing Models for Replicating Credit Ratings: A Multicriteria Approach
Computational Economics
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Journal of Global Optimization
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Genetic algorithm-based feature set partitioning for classification problems
Pattern Recognition
Expert Systems with Applications: An International Journal
Optimization of nearest neighbor classifiers via metaheuristic algorithms for credit risk assessment
Journal of Global Optimization
Identifying core sets of discriminatory features using particle swarm optimization
Expert Systems with Applications: An International Journal
Different metaheuristic strategies to solve the feature selection problem
Pattern Recognition Letters
Ant colony and particle swarm optimization for financial classification problems
Expert Systems with Applications: An International Journal
A Hybrid Bumble Bees Mating Optimization - GRASP Algorithm for Clustering
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Honey Bees Mating Optimization algorithm for financial classification problems
Applied Soft Computing
Enhancing the classification accuracy by scatter-search-based ensemble approach
Applied Soft Computing
Engineering optimizations via nature-inspired virtual bee algorithms
IWINAC'05 Proceedings of the First international work-conference on the Interplay Between Natural and Artificial Computation conference on Artificial Intelligence and Knowledge Engineering Applications: a bioinspired approach - Volume Part II
Cooperative bees swarm for solving the maximum weighted satisfiability problem
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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.