High-dimensional QSAR classification modeling based on improving black
hole algorithm
Abstract
High-dimensionality is one of the major problems which affect the
quality of the quantitative structure-activity (property) relationship
(QSAR/ QSPR) classification methods in chemometrics. Applying variable
selection is essential to improve the performance of the classification
task. Variable selection is well-known as an NP-hard optimization
problem. Various evolutionary algorithms are dedicated to solving this
problem in the literature. Recently, a black hole algorithm was
proposed, which has been successfully applied to solve various
continuous optimization problems. In this paper, a new time-varying
transfer function is proposed to improve the exploration and
exploitation capability of the binary black hole algorithm in selecting
the most relevant descriptors (variables) in QSAR/ QSPR classification
models with high classification accuracy and short computing time. Based
on seven benchmark biopharmaceutical datasets, the experimental results
reveal the capability of the proposed time-varying transfer function to
achieve high classification accuracy with minimizing the number of
selected descriptors and reducing the computational time.