Haza Nuzly Abdull Hamed

Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection

Recently, feature selection and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as gene expression microarray data. Gene expression microarray data comprises up to hundreds of thousands of features with relatively small sample size. Because learning algorithms usually do not work well with this kind of data, a challenge to reduce the data dimensionality arises. A huge number of gene selection are applied to select a subset of relevant features for model construction and to seek for better cancer classification performance. This paper presents the basic taxonomy of feature selection, and also reviews the state-of-the-art gene selection methods by grouping the literatures into three categories: supervised, unsupervised, and semi-supervised. The comparison of experimental results on top 5 representative gene expression datasets indicates that the classification accuracy of unsupervised and semi-supervised feature selection is competitive with supervised feature selection.

Semi-supervised SVM-based Feature Selection for Cancer Classification using Microarray Gene Expression Data

Gene expression data always suffer from the high dimensionality issue, therefore feature selection becomes a fundamental tool in the analysis of cancer classification. Basically, the data can be collected easily without providing the label information, which is quite useful in improving the accuracy of the classification. Label information usually difficult to obtain as the labelling processes are tedious, costly and error prone. Previous studies of gene selection are mostly dedicated to supervised and unsupervised approaches. Support vector machine SVM is a common supervised technique to address gene selection and cancer classification problems. Hence, this paper aims to propose a semi-supervised SVM-based feature selection S

Feature reduction using standard deviation with different subsets selection in sentiment analysis

^3

Multi-objective K-means evolving spiking neural network model based on differential evolution

VM-FS, which simultaneously exploit the knowledge from unlabelled and labelled data. Experimental results on the gene expression data of lung cancer show that S

The enhancement of evolving spiking neural network with dynamic population particle swarm optimization

^3

A New Hybrid K-Means Evolving Spiking Neural Network Model Based on Differential Evolution

VM-FS achieves the higher accuracy yet requires shorter processing time compares with the well-known supervised method, SVM-based recursive feature elimination SVM-RFE and the improved method, S