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Feature selection using feature dissimilarity measure and density-based clustering: application to biological data.

Debarka Sengupta¹, Indranil Aich, Sanghamitra Bandyopadhyay

¹Genome Institute of Singapore, Singapore 138 672, Singapore.

Journal of Biosciences

|November 14, 2015

Summary

A new unsupervised feature selection method uses maximum information compression index and DBSCAN to efficiently reduce dimensionality in complex biological models. This technique automatically identifies optimal features, improving model accuracy and speed.

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Area of Science:

Computational Biology
Bioinformatics
Machine Learning

Background:

Dimensionality reduction is crucial for modeling complex biological systems.
Numerous feature selection techniques exist to enhance model accuracy and speed.
Unsupervised methods are desirable for biological data where labels may be scarce.

Purpose of the Study:

To propose a novel unsupervised feature selection technique.
To leverage maximum information compression index and DBSCAN for dissimilarity measurement and feature grouping.
To automatically determine and identify the optimal subset of features for dimensionality reduction.

Main Methods:

Utilized maximum information compression index as a dissimilarity measure.
Employed the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm for cluster identification.
Applied the method to benchmark datasets of varying sizes for dimensionality reduction.

Main Results:

The proposed algorithm demonstrated speed and reduced sensitivity to user-defined parameters.
The method successfully identified the largest natural groups of dissimilar features.
Performance was extensively compared against established feature selection methods on benchmark datasets.

Conclusions:

The novel unsupervised feature selection technique offers an efficient approach to dimensionality reduction in biological modeling.
The method's ability to automatically determine feature subsets enhances its utility.
The approach shows promise for improving the performance of models dealing with complex biological data.