Machine Learning
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Minimum Redundancy Feature Selection from Microarray Gene Expression Data
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
No Unbiased Estimator of the Variance of K-Fold Cross-Validation
The Journal of Machine Learning Research
SoFoCles: Feature filtering for microarray classification based on Gene Ontology
Journal of Biomedical Informatics
A new dataset evaluation method based on category overlap
Computers in Biology and Medicine
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
RFS: Efficient feature selection method based on R-value
Computers in Biology and Medicine
A novel divide-and-merge classification for high dimensional datasets
Computational Biology and Chemistry
| Hi-index | 0.00 |
Classification analysis has been developed continuously since 1936. This research field has advanced as a result of development of classifiers such as KNN, ANN, and SVM, as well as through data preprocessing areas. Feature (gene) selection is required for very high dimensional data such as microarray before classification work. The goal of feature selection is to choose a subset of informative features that reduces processing time and provides higher classification accuracy. In this study, we devised a method of artificial gene making (AGM) for microarray data to improve classification accuracy. Our artificial gene was derived from a whole microarray dataset, and combined with a result of gene selection for classification analysis. We experimentally confirmed a clear improvement of classification accuracy after inserting artificial gene. Our artificial gene worked well for popular feature (gene) selection algorithms and classifiers. The proposed approach can be applied to any type of high dimensional dataset.