Input Variable Selection: Mutual Information and Linear Mixing Measures
IEEE Transactions on Knowledge and Data Engineering
Non-linear variable selection for artificial neural networks using partial mutual information
Environmental Modelling & Software
EURASIP Journal on Advances in Signal Processing
Expert Systems with Applications: An International Journal
Design of input vector for day-ahead price forecasting of electricity markets
Expert Systems with Applications: An International Journal
Normalized mutual information feature selection
IEEE Transactions on Neural Networks
Object detection using spatial histogram features
Image and Vision Computing
Energy Supervised Relevance Neural Gas for Feature Ranking
Neural Processing Letters
Informational energy kernel for LVQ
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
A filter based feature selection approach using lempel ziv complexity
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part II
Information Sciences: an International Journal
Investigating a novel GA-based feature selection method using improved KNN classifiers
International Journal of Information and Communication Technology
A new hybrid ant colony optimization algorithm for feature selection
Expert Systems with Applications: An International Journal
An excellent feature selection model using gradient-based and point injection techniques
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Feature selection for microarray data analysis using mutual information and rough set theory
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
International Journal of Data Warehousing and Mining
Expert Systems with Applications: An International Journal
A novel feature selection method and its application
Journal of Intelligent Information Systems
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A novel feature selection method using the concept of mutual information (MI) is proposed in this paper. In all MI based feature selection methods, effective and efficient estimation of high-dimensional MI is crucial. In this paper, a pruned Parzen window estimator and the quadratic mutual information (QMI) are combined to address this problem. The results show that the proposed approach can estimate the MI in an effective and efficient way. With this contribution, a novel feature selection method is developed to identify the salient features one by one. Also, the appropriate feature subsets for classification can be reliably estimated. The proposed methodology is thoroughly tested in four different classification applications in which the number of features ranged from less than 10 to over 15000. The presented results are very promising and corroborate the contribution of the proposed feature selection methodology.