Reducing bioinformatics data dimension with ABC-kNN

Analyzing a large amount of data often consumes extensive computational resources and execution time. However, sometime all data features do not equally contribute to the end results. Thus, it is plausible to identify the major contributing features and use them as representatives of the data. Other features with low contribution can be eliminated to reduce the time/resource consumption in data analysis. One of the promising application domains for such a feature selection process is bioinformatics. The need for dimension reduction, which is the process to reduce unnecessary features from the original data, arises because biological data can be massive, with tens of thousands of features to be explored. The objective of this study is to design an effective algorithm that can selectively remove irrelevant dimensions from data describing complex biological processes while preserving the semantics of the original data. This research proposes the adoption of the Artificial Bee Colony (ABC) as a novel method for data dimension reduction in classification problems. ABC, an efficient heuristic method based on swarm intelligence, is used to select the optimal subset of dimensions from the original high-dimensional data while retaining a subset that satisfies the defined objective. The k-Nearest Neighbor (kNN) method is then used for fitness evaluation within the ABC framework. In this research, ABC and kNN have been modified and bundled together to create an effective dimension reduction method. The proposed algorithm is validated in two distinct application domains: Gene expression analysis, and autistic behaviors study. The experimental results exhibit good solution quality as well as good computational performance.