# Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms
###### Abstract
The aforementioned FS methods have some limitations. The wrapper FS are efficient but time-consuming [21; 22]. The filter FS techniques are efficient and not computationally expensive. However, they are affected by the multicollinearity [23; 24]. The embedded FS methods are not well investigated compared to wrapper and filter FS methods due to their complex structure. The limitations of FS methods are deeply illustrated in Section 3. To overcome these limitations, this article proposes novel FS techniques stimulated by outlier detection where the outliers cover a small part of the dataset. Outlier detection involves searching for data objects that are different from standard data [25]. It is mainly required in numerous applications, including fraud detection, image processing, intrusion detection, public safety and security, and industry damage detection. In this article, the irrelevant features represent the outliers, and the dataset is represented as a matrix. The matrix is then transposed so that the rows represent the features (the attributes) and the columns stand for the samples. Thus, the outliers will be found in the samples of the transposed dataset, which are the features set of the original dataset. Outlier detection is performed using the Projection Pursuit (PP). The PP approach comprises looking for low dimensional linear projections that reveal potential outlying observations. Finding interesting projections is performed through the optimization of a specific function called a projection index (or PP index). The concept of this proposal is defined in Section 4. PP has successfully been applied to the search for outliers [26; 27], which is performed by assuming data normality and that the data not following the model are outliers. The present study focuses on developing FS based on outlier detection using PP called Projection Pursuit based Feature Selection (PPFS). To the author's knowledge, only one study used outlier detection for FS [28] but without PP. It involved feature ranking and isolation forest, which is different from this proposal. Therefore, this proposal is a new of its kind. The contribution of PPFS is described in the following section along with the research objectives. To validate this model, nineteen real-word datasets obtained from repositories are used along with three classifiers, including the k-Nearest Neighbors, Support Vector Machine, and Random Forest. Four classification measures are utilized, namely the accuracy, precision, F1 measure, and recall to confirm the results and compare them with those obtained with the state-of-the-art techniques. The article is structured as follows. Section 2 illustrates the contributions and the research objectives of this study. Section 3 surveys the existing works related to the four FS categories and addresses their limitations. Section 4 describes PP and the PP indices used in this study. Section 5 discusses the optimization methods applied to the PP indices. Section 6 presents the methodology followed in this study. Section 7 investigates the experimentation results, including the comparison study along with a deep discussion illustrating the results, the advantages and the limitations of this study, in addition to the research impacts. Section 8 concludes this work and presents some perspectives and future work.