Distributed Fuzzy Cognitive Maps for Feature Selection in Big Data Classification

The features of a dataset play an important role in the construction of a machine learning model. Because big datasets often have a large number of features, they may contain features that are less relevant to the machine learning task, which makes the process more time-consuming and complex. In ord...

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Bibliographic Details
Published in:Algorithms 2022-10, Vol.15 (10), p.383
Main Authors: Haritha, K., Judy, M. V., Papageorgiou, Konstantinos, Georgiannis, Vassilis C., Papageorgiou, Elpiniki
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Big Data
Classification
Cognitive maps
Cognitive models
Cognitive tasks
Datasets
Decision making
Decision trees
distributed fuzzy cognitive maps
Feature extraction
Feature selection
Fuzzy logic
genetic algorithms
Iterative methods
Machine learning
Management decisions
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Summary:The features of a dataset play an important role in the construction of a machine learning model. Because big datasets often have a large number of features, they may contain features that are less relevant to the machine learning task, which makes the process more time-consuming and complex. In order to facilitate learning, it is always recommended to remove the less significant features. The process of eliminating the irrelevant features and finding an optimal feature set involves comprehensively searching the dataset and considering every subset in the data. In this research, we present a distributed fuzzy cognitive map based learning-based wrapper method for feature selection that is able to extract those features from a dataset that play the most significant role in decision making. Fuzzy cognitive maps (FCMs) represent a hybrid computing technique combining elements of both fuzzy logic and cognitive maps. Using Spark’s resilient distributed datasets (RDDs), the proposed model can work effectively in a distributed manner for quick, in-memory processing along with effective iterative computations. According to the experimental results, when the proposed model is applied to a classification task, the features selected by the model help to expedite the classification process. The selection of relevant features using the proposed algorithm is on par with existing feature selection algorithms. In conjunction with a random forest classifier, the proposed model produced an average accuracy above 90%, as opposed to 85.6% accuracy when no feature selection strategy was adopted.
ISSN:1999-4893
1999-4893
DOI:10.3390/a15100383