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…

• UU
  Universiti Utara Malaysia
• MO
  Ministry of Higher Education, Malaysia
• UT
  Universiti Teknologi Malaysia