Floating search methods in feature selection
Pattern Recognition Letters
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive floating search methods in feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Selection for Knowledge Discovery and Data Mining
Feature Selection for Knowledge Discovery and Data Mining
An introduction to variable and feature selection
The Journal of Machine Learning Research
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
The use of receiver operating characteristic curves in biomedical informatics
Journal of Biomedical Informatics - Special issue: Clinical machine learning
A Direct Method of Nonparametric Measurement Selection
IEEE Transactions on Computers
A review of feature selection techniques in bioinformatics
Bioinformatics
A novel ensemble machine learning for robust microarray data classification
Computers in Biology and Medicine
Flexible-Hybrid sequential floating search in statistical feature selection
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
IEEE Transactions on Information Technology in Biomedicine
Knowledge discovery approach to automated cardiac SPECT diagnosis
Artificial Intelligence in Medicine
On the effectiveness of receptors in recognition systems
IEEE Transactions on Information Theory
ACACOS'11 Proceedings of the 10th WSEAS international conference on Applied computer and applied computational science
An efficient statistical feature selection approach for classification of gene expression data
Journal of Biomedical Informatics
Computers and Electronics in Agriculture
Identifying a small set of marker genes using minimum expected cost of misclassification
Artificial Intelligence in Medicine
Journal of Biomedical Informatics
Selection of interdependent genes via dynamic relevance analysis for cancer diagnosis
Journal of Biomedical Informatics
Journal of Biomedical Informatics
G-Optimal Feature Selection with Laplacian regularization
Neurocomputing
Hi-index | 0.03 |
This paper presents a novel feature selection approach to deal with issues of high dimensionality in biomedical data classification. Extensive research has been performed in the field of pattern recognition and machine learning. Dozens of feature selection methods have been developed in the literature, which can be classified into three main categories: filter, wrapper and hybrid approaches. Filter methods apply an independent test without involving any learning algorithm, while wrapper methods require a predetermined learning algorithm for feature subset evaluation. Filter and wrapper methods have their, respectively, drawbacks and are complementary to each other in that filter approaches have low computational cost with insufficient reliability in classification while wrapper methods tend to have superior classification accuracy but require great computational power. The approach proposed in this paper integrates filter and wrapper methods into a sequential search procedure with the aim to improve the classification performance of the features selected. The proposed approach is featured by (1) adding a pre-selection step to improve the effectiveness in searching the feature subsets with improved classification performances and (2) using Receiver Operating Characteristics (ROC) curves to characterize the performance of individual features and feature subsets in the classification. Compared with the conventional Sequential Forward Floating Search (SFFS), which has been considered as one of the best feature selection methods in the literature, experimental results demonstrate that (i) the proposed approach is able to select feature subsets with better classification performance than the SFFS method and (ii) the integrated feature pre-selection mechanism, by means of a new selection criterion and filter method, helps to solve the over-fitting problems and reduces the chances of getting a local optimal solution.