The nature of statistical learning theory
The nature of statistical learning theory
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Towards Optimal Feature and Classifier for Gene Expression Classification of Cancer
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
Gender Classification with Support Vector Machines
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Classification of heterogeneous gene expression data
ACM SIGKDD Explorations Newsletter
Multipopulation cooperative coevolutionary programming (MCCP) to enhance design innovation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Adaptive discriminant analysis for microarray-based classification
ACM Transactions on Knowledge Discovery from Data (TKDD)
Handling gene redundancy in microarray data using Grey Relational Analysis
International Journal of Data Mining and Bioinformatics
Molecular Diagnosis of Tumor Based on Independent Component Analysis and Support Vector Machines
Computational Intelligence and Security
Brief communication: Reducing multiclass cancer classification to binary by output coding and SVM
Computational Biology and Chemistry
A novel ensemble machine learning for robust microarray data classification
Computers in Biology and Medicine
A novel relative space based gene feature extraction and cancer recognition
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Feature extraction and classification of tumor based on wavelet package and support vector machines
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
Information Sciences: an International Journal
Hybrid methods to select informative gene sets in microarray data classification
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Artificial Intelligence in Medicine
On the effectiveness of gene selection for microarray classification methods
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
A class-specific ensemble feature selection approach for classification problems
Proceedings of the 48th Annual Southeast Regional Conference
Exploring features and classifiers to classify microRNA expression profiles of human cancer
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Gene selection for classifying microarray data using grey relation analysis
DS'06 Proceedings of the 9th international conference on Discovery Science
Combined gene selection methods for microarray data analysis
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
Clustering microarray data within amorphous computing paradigm and growing neural gas algorithm
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
SVM-Based tumor classification with gene expression data
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Bayesian learning of generalized gaussian mixture models on biomedical images
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
Feature selection for microarray data analysis using mutual information and rough set theory
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Robust ensemble learning for cancer diagnosis based on microarray data classification
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Improving gene selection in microarray data analysis using fuzzy patterns inside a CBR system
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Applying GCS networks to fuzzy discretized microarray data for tumour diagnosis
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
Data mining and genetic algorithm based gene/SNP selection
Artificial Intelligence in Medicine
Identification of micro RNA biomarkers for cancer by combining multiple feature selection techniques
Journal of Computational Methods in Sciences and Engineering
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The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it expectedly helps us to exactly predict and diagnose cancer. To precisely classify cancer we have to select genes related to cancer because extracted genes from microarray have many noises. In this paper, we attempt to explore many features and classifiers using three benchmark datasets to systematically evaluate the performances of the feature selection methods and machine learning classifiers. Three benchmark datasets are Leukemia cancer dataset, Colon cancer dataset and Lymphoma cancer data set. Pearson's and Spearman's correlation coefficients, Euclidean distance, cosine coefficient, information gain, mutual information and signal to noise ratio have been used for feature selection. Multi-layer perceptron, k-nearest neighbour, support vector machine and structure adaptive self-organizing map have been used for classification. Also, we have combined the classifiers to improve the performance of classification. Experimental results show that the ensemble with several basis classifiers produces the best recognition rate on the benchmark dataset.