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
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Classification Based Similarity Metric for 3D Image Retrieval
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Feature Selection for Clustering - A Filter Solution
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Feature selection based on rough sets and particle swarm optimization
Pattern Recognition Letters
Two-Step Particle Swarm Optimization to Solve the Feature Selection Problem
ISDA '07 Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications
A distributed PSO-SVM hybrid system with feature selection and parameter optimization
Applied Soft Computing
A wrapper method for feature selection using Support Vector Machines
Information Sciences: an International Journal
GSA: A Gravitational Search Algorithm
Information Sciences: an International Journal
An effective refinement strategy for KNN text classifier
Expert Systems with Applications: An International Journal
BGSA: binary gravitational search algorithm
Natural Computing: an international journal
Chaotic maps based on binary particle swarm optimization for feature selection
Applied Soft Computing
Principles of Visual Information Retrieval
Principles of Visual Information Retrieval
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
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Since given classification data often contains redundant, useless or misleading features, feature selection is an important pre-processing step for solving classification problems. This problem is often solved by applying evolutionary algorithms to decrease the dimensional number of features involved. Removing irrelevant features in the feature space and identifying relevant features correctly is the primary objective, which can increase classification accuracy. In this paper, a novel QBGSA-K-NN hybrid system which hybridizes the quantum-inspired binary gravitational search algorithm (QBGSA) with the K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) is proposed. The main aim of this system is to improve classification accuracy with an appropriate feature subset in binary problems. We evaluate the proposed hybrid system on several UCI machine learning benchmark examples. The experimental results show that the proposed method is able to select the discriminating input features correctly and achieve high classification accuracy which is comparable to or better than well-known similar classifier systems.