Bee for mining (B4M) – A novel rule discovery method using the Bees algorithm with quality-weight and coverage-weight

Abstract

This paper proposes a novel tool known as Bee for Mining (B4M) for classification tasks, which enables the Bees Algorithm (BA) to discover rules automatically. In the proposed B4M, two parameters namely quality-weight and coverage-weight have been added to the BA to avoid any ambiguous situations during the prediction phase. The contributions of the proposed B4M algorithm are two-fold: the first novel contribution is in the field of swarm intelligence, using a new version of BA for automatic rule discovery, and the second novel contribution is the formulation of a weight metric based on quailty and coverage of the rules discovered from the dataset to carry out Meta-Pruning and making it suitable for any classification problem in the real world. The proposed algorithm was implemented and tested using five different datasets from University of California, at Irvine (UCI Machine Learning Repository) and was compared with other well-known classification algorithms. The results obtained using the proposed B4M show that it was capable of achieving better classification accuracy and at the same time reduce the number of rules in four out of five UCI datasets. Furthermore, the results show that it was not only effective and more robust, but also more efficient, making it at least as good as other methods such as C5.0, C4.5, Jrip and other evolutionary algorithms, and in some cases even better.

Load Frequency Control Based on the Bees Algorithm for the Great Britain Power System

“…However, to the best of the authors' knowledge, no attempt has been made to utilize the BA in designing the secondary frequency control of a power system. Since this algorithm has demonstrated a high level of superiority and effectiveness as an optimization tool in many different fields [24][25][26][27], this promising achievement motivated the authors to make use of this very powerful optimization technique to determine the optimum parameters' gains of the PID, FOPID and Fuzzy PIDF for LFC in power systems. The main investigations of the present work are:…”

“…This section precisely summarizes the main concept in steps that explain the Bees Algorithm (BA). Since its invention by Pham et al in 2005 [24], several studies have been introduced based on this algorithm and different problems in many fields have been successfully solved using the BA [24][25][26][27]. The simplest pseudo-code for this algorithm is shown in Figure 5.…”

The Bees Algorithm Tuned Sliding Mode Control for Load Frequency Control in Two-Area Power System

“…Since its first introduction in 2005 by Professor Pham et al, this algorithm has been widely proposed by many researchers as a powerful optimisation tool to solve several problems in different fields. This is due to the wide spectrum of advantages such as simplicity and efficaciousness [33][34][35][36][37].…”