# Distributed Fuzzy Cognitive Maps for Feature Selection in Big Data Classification
## 1 Introduction  
In an age when big data is becoming increasingly popular, there is a huge amount of information that is redundant as well as irrelevant, which poses a challenge for academics and industry alike. The data that are gathered may be of high dimensionality, and, in most cases, not all of the features that are gathered are equally meaningful. Some of the features may be noisy, nonsensical, correlated, or unrelated to the task at hand. In most cases, high-dimensional data pose a problem for modeling tasks since the models are not always designed to cope with excessive amounts of inconsequential features, and this can reduce the performance of a predictive model. As a way of mitigating these problems, feature selection can identify relevant features and select them, eliminating nonessential or redundant features to maintain or improve classification accuracy [1]. Feature selection is a topic that has been studied intensively for a long time. Given the need to process massive amounts of data in a wide range of fields, the importance of this task has only grown. A second issue that practitioners must contend with is the lack of available computing resources. When dealing with huge amounts of data, the bulk of the approaches that are currently available for feature selection do not scale properly, and their efficiency may substantially decline to the point that they are no longer usable. Since present approachesare expected to prove inadequate for addressing the rising number of features found in big data, [PERSON] et al. (2018) concluded in their analysis of the most popular feature selection methods that a growing demand exists for scalable and efficient feature selection methods. Present approaches for feature selection are not likely to scale well when working with extensive data due to the fact that efficiency may considerably decline or the feature selection strategy may become inapplicable, which is the primary research need that has been identified.  
Fuzzy Cognitive Maps (FCMs) ([PERSON] and [PERSON], 1996) are systems inspired by human cognitive ability. FCMs employ a recursive learning procedure in order to learn about a given system and discover the various aspects of the system. An FCM is represented using a directed acyclic graph. The nodes in the graph represent the most important components of the system under consideration and the connections between the nodes represent the causal relationships between these concepts. Since FCMs represent the cause-effect relationships between attributes of a system, FCMs can be adopted to identify the features of a system that have maximum influence in the decision-making process. One of the major barriers in using FCMs is their inability to handle large datasets. This can be overcome by employing a distributed process to perform the FCM learning. The purpose of the current research was to look at the feasibility of using distributed FCMs to choose features from different data sources. The major contributions of this work are the following:  
* A novel fuzzy cognitive map based technique to extract the most significant features in a dataset that contribute to decision making was introduced.
* The proposed model was implemented in a distributed manner, thus enabling the scalability of the feature selection algorithm.
* Comparison of the performance of the proposed distributed fuzzy cognitive map feature selection algorithm with other best-performing algorithms was carried out.  
The paper is organized as follows: Section 2 provides insight into the current literature. Section 3 elaborates on the materials and methods used in the article. Section 4 demonstrates the results obtained and Section 5 is the performance analysis and discussion section. The paper's conclusion is presented in Section 6.