Random Projection-Based Feature Transformation Using Metaheuristic Optimization Algorithm

Abstract
Feature transformation methods can be used effectively to improve the performance of the machine learning techniques, such
as classification and clustering, via enhancing the discrimination among the samples. Additionally, binary representation of
features may be necessary in several circumstances. For example, distributed systems may have limited bandwidth, storage,
and energy that necessitate rough quantization of the measurements. This paper proposes a random projection-based feature
transformationmethod thatmaps the data points from the original space into a new binary space. In the proposed transformation
method, the random projection process is formulated as an optimization problem. A modified chaotic version of the sine–
cosine algorithm is used to find the optimal random projector. The performance of the proposed transformation method
is validated using 15 standard UCI benchmark datasets against the linear discernment analysis. Moreover, it is compared
to other metaheuristic-based feature reduction methods including the gray wolf optimizer, the genetic algorithm, and the
antlion optimizer. The obtained results assure the superiority of the proposed transformation method in terms of classification
accuracy and storage requirements.