Classification of Microarray Gene Expression Data Using an Infiltration Tactics Optimization (ITO) Algorithm

Abstract

A number of different feature selection and classification techniques have been proposed in literature including parameter-free and parameter-based algorithms. The former are quick but may result in local maxima while the latter use dataset-specific parameter-tuning for higher accuracy. However, higher accuracy may not necessarily mean higher reliability of the model. Thus, generalized optimization is still a challenge open for further research. This paper presents a warzone inspired "infiltration tactics" based optimization algorithm (ITO)-not to be confused with the ITO algorithm based on the Itõ Process in the field of Stochastic calculus. The proposed ITO algorithm combines parameter-free and parameter-based classifiers to produce a high-accuracy-high-reliability (HAHR) binary classifier. The algorithm produces results in two phases: (i) Lightweight Infantry Group (LIG) converges quickly to find non-local maxima and produces comparable results (i.e., 70 to 88% accuracy) (ii) Followup Team (FT) uses advanced tuning to enhance the baseline performance (i.e., 75 to 99%). Every soldier of the ITO army is a base model with its own independently chosen Subset selection method, pre-processing, and validation methods and classifier. The successful soldiers are combined through heterogeneous ensembles for optimal results. The proposed approach addresses a data scarcity problem, is flexible to the choice of heterogeneous base classifiers, and is able to produce HAHR models comparable to the established MAQC-II results.

Keywords: cancer; classification; clustering; computational intelligence; ensembles; infiltration; infiltration tactics optimization algorithm; machine learning; microarray.

Figure 1

LIG(i) filtered-in accuracy, MCC, and $\rho$.

Figure 2

$LI{G}_{Ensemble}$ vs LIG(i) filtered-in accuracy, MCC, and $\rho$.

Figure 3

FT(i) filtered-in accuracy, MCC, and $\rho$.

Figure 4

$F{T}_{Ensemble}$ vs. FT(i) filtered-in accuracy, MCC, and $\rho$.

Figure 5

Overall improved results produced by ITO Algorithm on all datasets.

Figure 6

Dataset A—LIG(i) filtered-in training times.

Figure 7

Dataset B—LIG(i) filtered-in training times.

Figure 8

Dataset C—LIG(i) filtered-in training times.

Figure 9

Dataset A—FT(i) filtered-in training times.

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Dataset B—FT(i) filtered-in training times.

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Dataset C—FT(i) filtered-in training times.