A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Floating search methods in feature selection
Pattern Recognition Letters
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
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
Optimization of nearest neighbor classifiers via metaheuristic algorithms for credit risk assessment
Journal of Global Optimization
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
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 colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Evolutionary selection of hyperrectangles in nested generalized exemplar learning
Applied Soft Computing
A honey bees mating optimization algorithm for the open vehicle routing problem
Proceedings of the 13th annual conference on Genetic and evolutionary computation
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
SAR image segmentation based on Artificial Bee Colony algorithm
Applied Soft Computing
An approach of bio-inspired hybrid model for financial markets
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Information Sciences: an International Journal
Discrete Artificial Bee Colony Optimization Algorithm for Financial Classification Problems
International Journal of Applied Metaheuristic Computing
Hi-index | 0.01 |
Nature inspired methods are approaches that are used in various fields and for the solution for a number of problems. This study uses a nature inspired method, namely Honey Bees Mating Optimization, that is based on the mating behaviour of honey 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 towards the development of accurate financial classification models involves the selection of the appropriate independent variables (features) which are relevant for the problem at hand. The proposed method uses for the feature selection step, the Honey Bees Mating Optimization algorithm while for the classification step, Nearest Neighbor based classifiers are used. The performance of the method is tested in a financial classification task involving credit risk assessment. The results of the proposed method are compared with the results of a particle swarm optimization algorithm, an ant colony optimization, a genetic algorithm and a tabu search algorithm.