Original Contribution: Stacked generalization
Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Machine Learning
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Generating Accurate Rule Sets Without Global Optimization
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper
Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference
Genetic Algorithms as a Tool for Restructuring Feature Space Representations
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Data Mining
A new classification model with simple decision rule for discovering optimal feature gene pairs
Computers in Biology and Medicine
Computers in Biology and Medicine
Rough Sets in Oligonucleotide Microarray Data Analysis
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Decision analysis of data mining project based on Bayesian risk
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Model of experts for decision support in the diagnosis of leukemia patients
Artificial Intelligence in Medicine
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Gene selection and cancer microarray data classification via mixed-integer optimization
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Predicting incomplete gene microarray data with the use of supervised learning algorithms
Pattern Recognition Letters
A novel cancer classifier based on differentially expressed gene network
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Gene expression data classification using locally linear discriminant embedding
Computers in Biology and Medicine
Lung cancer detection using labeled sputum sample: multi spectrum approach
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part II
Dietary patterns analysis using data mining method. An application to data from the CYKIDS study
Computer Methods and Programs in Biomedicine
Fuzzy expert system for predicting pathological stage of prostate cancer
Expert Systems with Applications: An International Journal
Performance evaluation of ranking methods for relevant gene selection in cancer microarray datasets
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Computers in Biology and Medicine
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
MaskedPainter: Feature selection for microarray data analysis
Intelligent Data Analysis
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Cancer leads to approximately 25% of all mortalities, making it the second leading cause of death in the United States. Early and accurate detection of cancer is critical to the well being of patients. Analysis of gene expression data leads to cancer identification and classification, which will facilitate proper treatment selection and drug development. Gene expression data sets for ovarian, prostate, and lung cancer were analyzed in this research. An integrated gene-search algorithm for genetic expression data analysis was proposed. This integrated algorithm involves a genetic algorithm and correlation-based heuristics for data preprocessing (on partitioned data sets) and data mining (decision tree and support vector machines algorithms) for making predictions. Knowledge derived by the proposed algorithm has high classification accuracy with the ability to identify the most significant genes. Bagging and stacking algorithms were applied to further enhance the classification accuracy. The results were compared with that reported in the literature. Mapping of genotype information to the phenotype parameters will ultimately reduce the cost and complexity of cancer detection and classification.