A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Large margin classification using the perceptron algorithm
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
BOAT—optimistic decision tree construction
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Further results on the margin distribution
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Tissue classification with gene expression profiles
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Using Bayesian networks to analyze expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
Class prediction and discovery using gene expression data
RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Advances in Large Margin Classifiers
Advances in Large Margin Classifiers
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Scaling Kernel-Based Systems to Large Data Sets
Data Mining and Knowledge Discovery
Incremental Induction of Decision Trees
Machine Learning
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
PUBLIC: A Decision Tree Classifier that Integrates Building and Pruning
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
SPRINT: A Scalable Parallel Classifier for Data Mining
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
On the Learnability and Design of Output Codes for Multiclass Problems
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Exploratory medical knowledge discovery: experiences and issues
ACM SIGKDD Explorations Newsletter
Classification of heterogeneous gene expression data
ACM SIGKDD Explorations Newsletter
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Two-stage classification methods for microarray data
Expert Systems with Applications: An International Journal
Gene expression modeling through positive boolean functions
International Journal of Approximate Reasoning
Handling gene redundancy in microarray data using Grey Relational Analysis
International Journal of Data Mining and Bioinformatics
Rough Sets in Oligonucleotide Microarray Data Analysis
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Novel Extension of k - TSP Algorithm for Microarray Classification
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
APPLYING DATA MINING TECHNIQUES FOR CANCER CLASSIFICATION ON GENE EXPRESSION DATA
Cybernetics and Systems
Information Sciences: an International Journal
Cancer classification using microarray and layered architecture genetic programming
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
An unsupervised clustering approach for leukaemia classification based on DNA micro-arrays data
Intelligent Data Analysis
Classification of oncologic data with genetic programming
Journal of Artificial Evolution and Applications - Special issue on artificial evolution methods in the biological and biomedical sciences
Artificial Intelligence in Medicine
Expert Systems with Applications: An International Journal
SoFoCles: Feature filtering for microarray classification based on Gene Ontology
Journal of Biomedical Informatics
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
Ensemble gene selection for cancer classification
Pattern Recognition
On the use of genetic programming for the prediction of survival in cancer
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Robust approach for estimating probabilities in Naïve-Bayes Classifier for gene expression data
Expert Systems with Applications: An International Journal
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
An efficient statistical feature selection approach for classification of gene expression data
Journal of Biomedical Informatics
A new gene selection method based on random subspace ensemble for microarray cancer classification
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
Gene selection for classifying microarray data using grey relation analysis
DS'06 Proceedings of the 9th international conference on Discovery Science
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Virtual gene: using correlations between genes to select informative genes on microarray datasets
Transactions on Computational Systems Biology II
Pathway-based microarray analysis with negatively correlated feature sets for disease classification
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Expert Systems with Applications: An International Journal
Global top-scoring pair decision tree for gene expression data analysis
EuroGP'13 Proceedings of the 16th European conference on Genetic Programming
Combining information extraction and text mining for cancer biomarker detection
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
PLS-based recursive feature elimination for high-dimensional small sample
Knowledge-Based Systems
Diverse accurate feature selection for microarray cancer diagnosis
Intelligent Data Analysis
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The classification of different tumor types is of great importance in cancer diagnosis and drug discovery. However, most previous cancer classification studies are clinical based and have limited diagnostic ability. Cancer classification using gene expression data is known to contain the keys for addressing the fundamental problems relating to cancer diagnosis and drug discovery. The recent advent of DNA microarray technique has made simultaneous monitoring of thousands of gene expressions possible. With this abundance of gene expression data, researchers have started to explore the possibilities of cancer classification using gene expression data. Quite a number of methods have been proposed in recent years with promising results. But there are still a lot of issues which need to be addressed and understood.In order to gain a deep insight into the cancer classification problem, it is necessary to take a closer look at the problem, the proposed solutions and the related issues all together. In this survey paper, we present a comprehensive overview of various proposed cancer classification methods and evaluate them based on their computation time, classification accuracy and ability to reveal biologically meaningful gene information. We also introduce and evaluate various proposed gene selection methods which we believe should be an integral preprocessing step for cancer classification. In order to obtain a full picture of cancer classification, we also discuss several issues related to cancer classification, including the biological significance vs. statistical significance of a cancer classifier, the asymmetrical classification errors for cancer classifiers, and the gene contamination problem.