Interpretable gene expression classifier with an accurate and compact fuzzy rule base for microarray data analysis.
An accurate classifier with linguistic interpretability using a small number of relevant genes is beneficial to microarray data analysis and development of inexpensive diagnostic tests. Several frequently used techniques for designing classifiers of microarray data, such as support vector machine, neural networks, k-nearest neighbor, and logistic regression model, suffer from low interpretabilities. This paper proposes an interpretable gene expression classifier (named iGEC) with an accurate and compact fuzzy rule base for microarray data analysis. The design of iGEC has three objectives to be simultaneously optimized: maximal classification accuracy, minimal number of rules, and minimal number of used genes. An "intelligent" genetic algorithm IGA is used to efficiently solve the design problem with a large number of tuning parameters. The performance of iGEC is evaluated using eight commonly-used data sets. It is shown that iGEC has an accurate, concise, and interpretable rule base (1.1 rules per class) on average in terms of test classification accuracy (87.9%), rule number (3.9), and used gene number (5.0). Moreover, iGEC not only has better performance than the existing fuzzy rule-based classifier in terms of the above-mentioned objectives, but also is more accurate than some existing non-rule-based classifiers.
Ho
Shinn-Ying
SY
Department of Biological Science and Technology, National Chiao Tung University, Hsinchu, Taiwan. syho@mail.nctu.edu.tw
Hsieh
Chih-Hung
CH
Chen
Hung-Ming
HM
Huang
Hui-Ling
HL
D000465:Algorithms; D019295:Computational Biology; D017143:Fuzzy Logic; D015870:Gene Expression; D020869:Gene Expression Profiling; D006801:Humans; D007938:Leukemia / Q000235:genetics; D008175:Lung Neoplasms / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012680:Sensitivity and Specificity