# Distributed Fuzzy Cognitive Maps for Feature Selection in Big Data Classification
## 2 Literature Review  
A feature selection approach does not alter the original representation of the variables being analyzed; rather, it only selects a subset of them, as opposed to other dimensionality reduction approaches, such as those based on projection or compression. In this way, the variables retain their original meaning by preserving their original semantics and offering the advantage of being able to be interpreted by domain experts. Features can serve a variety of purposes, but the most important goals are (a) to reduce overfitting and improve model performance, that is, to achieve better prediction performance in supervised classification and better cluster-detecting performance in clustering; (b) to develop faster and more cost-effective models; and (c) to gain an understanding of the processes that produced the data. In contrast, the advantages of feature selection techniques are not without their downsides, as searching for a subset of relevant features introduces another layer of complexity to the modeling task. When the class labels of a feature set are known, feature selection strategies are categorized as supervised, and they are categorized as unsupervised when the class labels are unknown. There are three types of supervised feature selection strategy used in classification: filter, wrapper, and embedded methods ([PERSON] and [PERSON], 1996). The filter approach works as a preprocessing step and utilizes the general characteristics of the training data independent of the predictive model being used ([PERSON] and [PERSON], 1996). The wrapper selection method creates many models with different subsets of input features and selects the ones that yield the best performance ([PERSON] and [PERSON], 1996). A feature selection procedure that is embedded in a model's training process is termed an embedded approach. [PERSON] et al. (2019) investigated different ensemble feature selection techniques, which pool the strengths of different feature selection approaches to provide more reliable outcomes. [PERSON] et al. (2018) proposed an ensemble-learning-based feature selection to enhance the performance of micro-arraydata classification. There are other techniques that have been combined with feature selection, such as tree ensemble [8] and feature extraction [9]. [PERSON] et al. suggested a two-stage feature selection algorithm by combining ReliefF and mRMR [10], while [PERSON] et al. [11] proposed a two-stage feature selection algorithm for genomic data by combining Minimum Redundancy-Maximum Relevance and Genetic Algorithm methods to obtain the optimal feature subset. [PERSON] et al. [12] discussed three different approaches for feature selection in large-scale industrial processes for soft sensor construction. [PERSON] et al. [13] presented a method to improve the performance of open source software data prediction using Bayesian classification. [PERSON] et al. [14] discussed the use of an auto-encoder for dimensionality reduction based on bunch graphs. A new feature selection technique with ensemble learning, introduced by [PERSON] et al. [15], converts the feature selection procedure into a multicriterion decision-making problem that is subsequently analyzed using the VICOR method. [PERSON] et al. [16] addressed the problem of feature selection as an aggregate of three cutting-edge filtration techniques: the linear correlation coefficient of Pearson, the ReliefF algorithm, and decision trees. [PERSON] et al. [17] discussed the feature selection mechanisms available in the Apache Spark platform. Feature selection algorithms have been used in many real-world applications in the available literature, including intrusion detection [18], text categorization [19], email classification [20] microarray analysis [6], [21], information retrieval [22], etc. [PERSON] introduced the concept of fuzzy cognitive maps in 1986 [3] as an extension of the [PERSON]'s Cognitive Map proposal from 1976 [23]. FCMs provide a framework for representing complex systems by representing their components and causal relationships.