Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
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
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Feature subset selection by Bayesian network-based optimization
Artificial Intelligence
Machine Learning
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Analysis of gene expression profiles: class discovery and leaf ordering
Proceedings of the sixth annual international conference on Computational biology
Machine Learning
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Simultaneous Relevant Feature Identification and Classification in High-Dimensional Spaces
WABI '02 Proceedings of the Second International Workshop on Algorithms in Bioinformatics
Selection of Informative Genes in Gene Expression Based Diagnosis: A Nonparametric Approach
ISMDA '00 Proceedings of the First International Symposium on Medical Data Analysis
Gene selection by sequential search wrapper approaches in microarray cancer class prediction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Challenges for future intelligent systems in biomedicine
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Feature subset selection by genetic algorithms and estimation of distribution algorithms
Artificial Intelligence in Medicine
Gene subset selection in kernel-induced feature space
Pattern Recognition Letters
Support Vector Based T-Score for Gene Ranking
PRIB '08 Proceedings of the Third IAPR International Conference on Pattern Recognition in Bioinformatics
F-score with Pareto Front Analysis for Multiclass Gene Selection
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Gene boosting for cancer classification based on gene expression profiles
Pattern Recognition
Artificial Intelligence in Medicine
International Journal of Bioinformatics Research and Applications
Gene selection from microarray data for cancer classification-a machine learning approach
Computational Biology and Chemistry
Microarray analysis of autoimmune diseases by machine learning procedures
IEEE Transactions on Information Technology in Biomedicine
A Multiple-Filter-Multiple-Wrapper Approach to Gene Selection and Microarray Data Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Noise-based feature perturbation as a selection method for microarray data
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Knowledge discovery and emergent complexity in bioinformatics
KDECB'06 Proceedings of the 1st international conference on Knowledge discovery and emergent complexity in bioinformatics
SVM-RFE with relevancy and redundancy criteria for gene selection
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Rule acquisition for cognitive agents by using estimation of distribution algorithms
International Journal of Knowledge Engineering and Soft Data Paradigms
Improving the Computational Efficiency of Recursive Cluster Elimination for Gene Selection
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Identifying user preferences with Wrapper-based Decision Trees
Expert Systems with Applications: An International Journal
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Gene ranking from microarray data for cancer classification: a machine learning approach
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Data mining of gene expression microarray via weighted prefix trees
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Feature selection method using WF-LASSO for gene expression data analysis
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Heuristic search over a ranking for feature selection
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Evidence accumulation to identify discriminatory signatures in biomedical spectra
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Decision making association rules for recognition of differential gene expression profiles
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Two way focused classification
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Informative gene selection and tumor classification by null space LDA for microarray data
ESCAPE'07 Proceedings of the First international conference on Combinatorics, Algorithms, Probabilistic and Experimental Methodologies
Simultaneous sample and gene selection using t-score and approximate support vectors
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
A heuristic biomarker selection approach based on professional tennis player ranking strategy
Computer Methods and Programs in Biomedicine
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DNA microarray experiments generating thousands of gene expression measurements, are used to collect information from tissue and cell samples regarding gene expression differences that could be useful for diagnosis disease, distinction of the specific tumor type, etc. One important application of gene expression microarray data is the classification of samples into known categories. As DNA microarray technology measures the gene expression en masse, this has resulted in data with the number of features (genes) far exceeding the number of samples. As the predictive accuracy of supervised classifiers that try to discriminate between the classes of the problem decays with the existence of irrelevant and redundant features, the necessity of a dimensionality reduction process is essential. We propose the application of a gene selection process, which also enables the biology researcher to focus on promising gene candidates that actively contribute to classification in these large scale microarrays. Two basic approaches for feature selection appear in machine learning and pattern recognition literature: the filter and wrapper techniques. Filter procedures are used in most of the works in the area of DNA microarrays. In this work, a comparison between a group of different filter metrics and a wrapper sequential search procedure is carried out. The comparison is performed in two well-known DNA microarray datasets by the use of four classic supervised classifiers. The study is carried out over the original-continuous and three-intervals discretized gene expression data. While two well-known filter metrics are proposed for continuous data, four classic filter measures are used over discretized data. The same wrapper approach is used for both continuous and discretized data. The application of filter and wrapper gene selection procedures leads to considerably better accuracy results in comparison to the non-gene selection approach, coupled with interesting and notable dimensionality reductions. Although the wrapper approach mainly shows a more accurate behavior than filter metrics, this improvement is coupled with considerable computer-load necessities. We note that most of the genes selected by proposed filter and wrapper procedures in discrete and continuous microarray data appear in the lists of relevant-informative genes detected by previous studies over these datasets. The aim of this work is to make contributions in the field of the gene selection task in DNA microarray datasets. By an extensive comparison with more popular filter techniques, we would like to make contributions in the expansion and study of the wrapper approach in this type of domains.