Particle Swarm Optimized Ensemble Learning for Enhanced Predictive Sentiment Accuracy of Tweets

This paper proposes a heterogeneous ensemble classifier configuration for a multiclass intrusion detection problem. The ensemble is composed of k-Nearest Neighbors (kNN), Artificial Neural Networks (ANN), and Naïve Bayes (NB) classifiers. The decisions of these classifiers are combined with Weighted Majority Voting (WMV), where optimal weights are generated by Ant Colony Optimization for continuous search spaces (ACOR). As a comparison basis, we have also implemented the ensemble configuration with the unweighted majority voting or Winner Takes All (WTA) strategy. To ensure the maximum variety of classifiers, we have implemented three versions of each classification algorithm by varying each classifier's parameters making a total of nine diverse experts for the ensemble. For our empirical study, we used the

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Particle Swarm Optimized Ensemble Learning for Enhanced Predictive Sentiment Accuracy of Tweets

Cited by 4 publications

References 27 publications

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Weighted heterogeneous ensemble for the classification of intrusion detection using ant colony optimization for continuous search spaces

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