Instance-Based Learning Algorithms
Machine Learning
A practical approach to feature selection
ML92 Proceedings of the ninth international workshop on Machine learning
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Floating search methods in feature selection
Pattern Recognition Letters
The nature of statistical learning theory
The nature of statistical learning theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Machine Learning - Special issue on learning with probabilistic representations
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
Machine Learning
Feature Subset Selection and Order Identification for Unsupervised Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Cancer classification using gene expression data
Information Systems - Special issue: Data management in bioinformatics
Chi2: Feature Selection and Discretization of Numeric Attributes
TAI '95 Proceedings of the Seventh International Conference on Tools with Artificial Intelligence
An introduction to variable and feature selection
The Journal of Machine Learning Research
Cancer classification and prediction using logistic regression with Bayesian gene selection
Journal of Biomedical Informatics - Special issue: Biomedical machine learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-stage classification methods for microarray data
Expert Systems with Applications: An International Journal
A hybrid genetic algorithm for feature selection wrapper based on mutual information
Pattern Recognition Letters
A review of feature selection techniques in bioinformatics
Bioinformatics
New gene selection method for multiclass tumor classification by class centroid
Journal of Biomedical Informatics
Kernel based nonlinear dimensionality reduction for microarray gene expression data analysis
Expert Systems with Applications: An International Journal
An expert system to classify microarray gene expression data using gene selection by decision tree
Expert Systems with Applications: An International Journal
A sequential feature extraction approach for naïve bayes classification of microarray data
Expert Systems with Applications: An International Journal
A novel feature selection approach for biomedical data classification
Journal of Biomedical Informatics
Ensemble gene selection by grouping for microarray data classification
Journal of Biomedical Informatics
Robust approach for estimating probabilities in naive-Bayes classifier
PReMI'07 Proceedings of the 2nd international conference on Pattern recognition and machine intelligence
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Selection of interdependent genes via dynamic relevance analysis for cancer diagnosis
Journal of Biomedical Informatics
Journal of Biomedical Informatics
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Classification of gene expression data plays a significant role in prediction and diagnosis of diseases. Gene expression data has a special characteristic that there is a mismatch in gene dimension as opposed to sample dimension. All genes do not contribute for efficient classification of samples. A robust feature selection algorithm is required to identify the important genes which help in classifying the samples efficiently. In order to select informative genes (features) based on relevance and redundancy characteristics, many feature selection algorithms have been introduced in the past. Most of the earlier algorithms require computationally expensive search strategy to find an optimal feature subset. Existing feature selection methods are also sensitive to the evaluation measures. The paper introduces a novel and efficient feature selection approach based on statistically defined effective range of features for every class termed as ERGS (Effective Range based Gene Selection). The basic principle behind ERGS is that higher weight is given to the feature that discriminates the classes clearly. Experimental results on well-known gene expression datasets illustrate the effectiveness of the proposed approach. Two popular classifiers viz. Nave Bayes Classifier (NBC) and Support Vector Machine (SVM) have been used for classification. The proposed feature selection algorithm can be helpful in ranking the genes and also is capable of identifying the most relevant genes responsible for diseases like leukemia, colon tumor, lung cancer, diffuse large B-cell lymphoma (DLBCL), prostate cancer.