Recursive distributed representations
Artificial Intelligence - On connectionist symbol processing
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Elements of information theory
Elements of information theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Input Feature Extraction for Multilayered Perceptrons Using Supervised Principal Component Analysis
Neural Processing Letters
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Introduction to the Theory of Neural Computation
Introduction to the Theory of Neural Computation
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
PAC Meditation on Boolean Formulas
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Non-linear dimensionality reduction techniques for classification and visualization
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
Experiments with random projections for machine learning
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Redundancy based feature selection for microarray data
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Supervised Clustering " Algorithms and Benefits
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
BICA: a Boolean Independent Component Analysis Algorithm
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Significance of Gene Ranking for Classification of Microarray Samples
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Genetic algorithms for linear feature extraction
Pattern Recognition Letters
Feature Extraction from Microarray Expression Data by Integration of Semantic Knowledge
ICMLA '07 Proceedings of the Sixth International Conference on Machine Learning and Applications
The weighted majority algorithm
SFCS '89 Proceedings of the 30th Annual Symposium on Foundations of Computer Science
Cluster analysis of genome-wide expression data for feature extraction
Expert Systems with Applications: An International Journal
Neurocomputing
Which is the best multiclass SVM method? an empirical study
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
MML inference of oblique decision trees
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Cognitive Systems Research
A new gradient-based neural network for solving linear and quadratic programming problems
IEEE Transactions on Neural Networks
A general framework for learning rules from data
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis
Information Sciences: an International Journal
Applying electromagnetism-like mechanism for feature selection
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
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We devise a feature selection method in terms of a follow-out utility of a special classification procedure. In turn, we root the latter on binary features which we extract from the input patterns with a wrapper method. The whole contrivance results in a procedure that is progressive in two respects. As for features, first we compute a very essential representation of them in terms of Boolean independent components in order to reduce their entropy. Then we reverse the representation mapping to discover the subset of the original features supporting a successful classification. As for the classification, we split it into two less hard tasks. With the former we look for a clustering of input patterns that satisfies loose consistency constraints and benefits from the conciseness of binary representation. With the latter we attribute labels to the clusters through the combined use of basically linear separators. We implement out the method through a relatively quick numerical procedure by assembling a set of connectionist and symbolic routines. These we toss on the benchmark of feature selection of DNA microarray data in cancer diagnosis and other ancillary datasets.