Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Floating search methods in feature selection
Pattern Recognition Letters
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Subset Selection Using a Genetic Algorithm
IEEE Intelligent Systems
Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Benchmarking Attribute Selection Techniques for Discrete Class Data Mining
IEEE Transactions on Knowledge and Data Engineering
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Binary Feature Selection with Conditional Mutual Information
The Journal of Machine Learning Research
Not So Naive Bayes: Aggregating One-Dependence Estimators
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Computers and Electronics in Agriculture
A filter model for feature subset selection based on genetic algorithm
Knowledge-Based Systems
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Finding key attribute subset in dataset for outlier detection
Knowledge-Based Systems
Breeding value classification in manchego sheep: a study of attribute selection and construction
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Global feature subset selection on high-dimensional datasets using re-ranking-based EDAs
CAEPIA'11 Proceedings of the 14th international conference on Advances in artificial intelligence: spanish association for artificial intelligence
A two-grade approach to ranking interval data
Knowledge-Based Systems
Simple instance selection for bankruptcy prediction
Knowledge-Based Systems
Face recognition using discriminant sparsity neighborhood preserving embedding
Knowledge-Based Systems
Entropic feature discrimination ability for pattern classification based on neural IAL
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Feature selection using dynamic weights for classification
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Hi-index | 0.00 |
This paper deals with the problem of supervised wrapper-based feature subset selection in datasets with a very large number of attributes. Recently the literature has contained numerous references to the use of hybrid selection algorithms: based on a filter ranking, they perform an incremental wrapper selection over that ranking. Though working fine, these methods still have their problems: (1) depending on the complexity of the wrapper search method, the number of wrapper evaluations can still be too large; and (2) they rely on a univariate ranking that does not take into account interaction between the variables already included in the selected subset and the remaining ones. Here we propose a new approach whose main goal is to drastically reduce the number of wrapper evaluations while maintaining good performance (e.g. accuracy and size of the obtained subset). To do this we propose an algorithm that iteratively alternates between filter ranking construction and wrapper feature subset selection (FSS). Thus, the FSS only uses the first block of ranked attributes and the ranking method uses the current selected subset in order to build a new ranking where this knowledge is considered. The algorithm terminates when no new attribute is selected in the last call to the FSS algorithm. The main advantage of this approach is that only a few blocks of variables are analyzed, and so the number of wrapper evaluations decreases drastically. The proposed method is tested over eleven high-dimensional datasets (2400-46,000 variables) using different classifiers. The results show an impressive reduction in the number of wrapper evaluations without degrading the quality of the obtained subset.