Pattern recognition: human and mechanical
Pattern recognition: human and mechanical
Boolean Feature Discovery in Empirical Learning
Machine Learning
Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
C4.5: programs for machine learning
C4.5: programs for machine learning
Deliberation scheduling for problem solving in time-constrained environments
Artificial Intelligence
Learning Boolean concepts in the presence of many irrelevant features
Artificial Intelligence
Fundamentals of algorithmics
Wrappers for performance enhancement and oblivious decision graphs
Wrappers for performance enhancement and oblivious decision graphs
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Feature Selection Using Rough Sets Theory
ECML '93 Proceedings of the European Conference on Machine Learning
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
The Supervised Network Self-Organizing Map for Classification of Large Data Sets
Applied Intelligence
Synthesizing High-Frequency Rules from Different Data Sources
IEEE Transactions on Knowledge and Data Engineering
A comparative study for domain ontology guided feature extraction
ACSC '03 Proceedings of the 26th Australasian computer science conference - Volume 16
Dimensionality Reduction in Automatic Knowledge Acquisition: A Simple Greedy Search Approach
IEEE Transactions on Knowledge and Data Engineering
The bitmap-based feature selection method
Proceedings of the 2003 ACM symposium on Applied computing
Pattern Recognition Letters
A novel feature selection method for large-scale data sets
Intelligent Data Analysis
Information Sciences: an International Journal
An efficient bit-based feature selection method
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Association rule mining: models and algorithms
Association rule mining: models and algorithms
Improving dynamic facial expression recognition with feature subset selection
Pattern Recognition Letters
A dimensionality reduction based on feature quality measure
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
A coevolutionary approach to optimize class boundaries for multidimensional classification problems
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
| Hi-index | 0.00 |
Feature selection is a problem of finding relevant features.When the number of features of a dataset is large and its number of patternsis huge, an effective method of feature selection can help in dimensionalityreduction. An incremental probabilistic algorithm is designed and implementedas an alternative to the exhaustive and heuristic approaches. Theoretical analysis is given to support the idea of the probabilistic algorithm in finding an optimal or near-optimal subset of features. Experimental results suggest that (1) the probabilistic algorithm is effective in obtaining optimal/suboptimal feature subsets; (2) its incremental version expedites feature selection further when the number of patterns is largeand can scale up without sacrificing the quality of selected features.