Characteristic attributes in cancer microarrays
Journal of Biomedical Informatics
Cancer classification and prediction using logistic regression with Bayesian gene selection
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
Predictive neural networks for gene expression data analysis
Neural Networks
Pattern classification in DNA microarray data of multiple tumor types
Pattern Recognition
Expert Systems with Applications: An International Journal
Computational Biology and Chemistry
Accurate Cancer Classification Using Expressions of Very Few Genes
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A new classification model with simple decision rule for discovering optimal feature gene pairs
Computers in Biology and Medicine
Artificial Intelligence in Medicine
Active learning for microarray data
International Journal of Approximate Reasoning
Constructing the gene regulation-level representation of microarray data for cancer classification
Journal of Biomedical Informatics
Wrapper filtering criteria via linear neuron and kernel approaches
Computers in Biology and Medicine
Power signal classification using dynamic wavelet network
Applied Soft Computing
New gene selection method for multiclass tumor classification by class centroid
Journal of Biomedical Informatics
Computational Statistics & Data Analysis
Fuzzy ensemble clustering based on random projections for DNA microarray data analysis
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Identification of critical genes in microarray experiments by a Neuro-Fuzzy approach
Computational Biology and Chemistry
Gene selection from microarray data for cancer classification-a machine learning approach
Computational Biology and Chemistry
A novel ensemble machine learning for robust microarray data classification
Computers in Biology and Medicine
Evolving connectionist systems for knowledge discovery from gene expression data of cancer tissue
Artificial Intelligence in Medicine
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
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In clinical practice, diagnostic dilemmas are frequently encountered in discriminating the heterogeneous cancers into distinct types. This paper reports an improved machine learning approach based on the wavelet neural network (WNN), which associates a feature selection method namely, the conditional T-test. It is used in the development of cancer classification by using benchmark microarray data. The experimental results showed that the proposed classifiers achieved a superior accuracy, which ranges from 92% to 100%. Performance comparisons are also made with other classifiers which show that this proposed approach outperforms most of them.