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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 transformation method that maps 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. [ABSTRACT FROM AUTHOR]

A major problem text classification faces is the high dimensional feature space of the text data. Feature selection (FS) algorithms are used for eliminating the irrelevant and redundant terms, thus increasing accuracy and speed of a text classifier. For text classification, FS algorithms have to be designed keeping the highly imbalanced classes of the text data in view. To this end, more recently ensemble algorithms (e.g., improved global feature selection scheme (IGFSS) and variable global feature selection scheme (VGFSS)) were proposed. These algorithms, which combine local and global FS metrics, have shown promising results with VGFSS having better capability of addressing the class imbalance issue. However, both these schemes are highly dependent on the underlying local and global FS metrics. Existing FS metrics get confused while selecting relevant terms of a data with highly imbalanced classes. In this paper, we propose a new FS metric named inherent distinguished feature selector (IDFS), which selects terms having greater relevance to classes and is highly effective for imbalanced data sets. We compare performance of IDFS against five well-known FS metrics as a stand-alone FS algorithm and as a part of the IGFSS and VGFSS frameworks on five benchmark data sets using two classifiers, namely support vector machines and random forests. Our results show that IDFS in both scenarios selects smaller subsets, and achieves higher micro and macro F 1 values, thus outperforming the existing FS metrics. [ABSTRACT FROM AUTHOR]