Training knowledge-based neural networks to recognize genes in DNA sequences
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
C4.5: programs for machine learning
C4.5: programs for machine learning
Learning optimal chess strategies
Machine intelligence 13
Synthesis of Parallel Algorithms
Synthesis of Parallel Algorithms
Combinatorial Algorithms: For Computers and Hard Calculators
Combinatorial Algorithms: For Computers and Hard Calculators
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Concept acquisition through representational adjustment
Concept acquisition through representational adjustment
Feature extraction by non parametric mutual information maximization
The Journal of Machine Learning Research
Testing the significance of attribute interactions
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hybrid genetic algorithm for feature selection wrapper based on mutual information
Pattern Recognition Letters
Feature selection with dynamic mutual information
Pattern Recognition
Principal component analysis of binary data by iterated singular value decomposition
Computational Statistics & Data Analysis
Normalized mutual information feature selection
IEEE Transactions on Neural Networks
Estimating redundancy information of selected features in multi-dimensional pattern classification
Pattern Recognition Letters
Knowledge discovery approach to automated cardiac SPECT diagnosis
Artificial Intelligence in Medicine
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Nearest neighbor estimate of conditional mutual information in feature selection
Expert Systems with Applications: An International Journal
Searching for a common pooling pattern among several samples
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
An algorithm is proposed for calculating correlation measures based on entropy. The proposed algorithm allows exhaustive exploration of variable subsets on real data. Its time efficiency is demonstrated by comparison against three other variable selection methods based on entropy using 8 data sets from various domains as well as simulated data. The method is applicable to discrete data with a limited number of values making it suitable for medical diagnostic support, DNA sequence analysis, psychometry and other domains.