Elements of information theory
Elements of information theory
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Feature Selection Applied to Content-Based Retrieval of Lung Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
An extensive empirical study of feature selection metrics for text classification
The Journal of Machine Learning Research
Consistency-based search in feature selection
Artificial Intelligence
Feature selection with conditional mutual information maximin in text categorization
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
A New Dependency and Correlation Analysis for Features
IEEE Transactions on Knowledge and Data Engineering
Feature subset selection bias for classification learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Consistent Feature Selection for Pattern Recognition in Polynomial Time
The Journal of Machine Learning Research
A new mutual information based measure for feature selection
Intelligent Data Analysis
Feature selection with dynamic mutual information
Pattern Recognition
Searching for interacting features
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Effective feature selection scheme using mutual information
Neurocomputing
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
| Hi-index | 12.05 |
The inclusion of irrelevant, redundant, and inconsistent features in the data-mining model results in poor predictions and high computational overhead. This paper proposes a novel information theoretic-based interact (IT-IN) algorithm, which concerns the relevance, redundancy, and consistency of the features. The proposed IT-IN algorithm is compared with existing Interact, FCBF, Relief and CFS feature selection algorithms. To evaluate the classification accuracy of IT-IN and remaining four feature selection algorithms, Naive Bayes, SVM, and ELM classifier are used for ten UCI repository datasets. The proposed IT-IN performs better than existing above algorithms in terms of number of features. The specially designed hash function is used to speed up the IT-IN algorithms and provides minimum computation time than the Interact algorithms. The result clearly reveals that the proposed feature selection algorithm improves the classification accuracy for ELM, Naive Bayes, and SVM classifiers. The performance of proposed IT-IN with ELM classifier is superior to other classifiers.