title: Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection.
abstract: 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.
authors.forename: Jun Chin
authors.initials: JC
authors.lastname: Ang
authors.forename: Andri
authors.initials: A
authors.lastname: Mirzal
authors.forename: Habibollah
authors.initials: H
authors.lastname: Haron
authors.forename: Haza Nuzly Abdull
authors.initials: HN
authors.lastname: Hamed
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods; D030541:Databases, Genetic; D052138:Genes, Neoplasm / Q000235:genetics*; D005819:Genetic Markers / Q000235:genetics; D020022:Genetic Predisposition to Disease / Q000235:genetics; D006801:Humans; D000069550:Machine Learning; D009363:Neoplasm Proteins / Q000235:genetics*; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D005819:Genetic Markers; D009363:Neoplasm Proteins