Binary Representation of Polar Bear Algorithm for Feature Selection

Mirkhan, Amer; 莈lebi, Numan

doi:10.32604/csse.2022.023249

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Some researchers have extended Pawlak's idea by incorporating fuzzy equivalence relations, neighborhood relations and dominance relations into Pawlak rough sets to form neighborhood rough sets [3,4], fuzzy rough sets [5][6][7][8][9], and dominance-based rough sets [10][11][12]. The generalized models of rough set are commonly applied in the reduction of attributes [13][14][15], feature selection [16][17][18][19], extraction of rules [20][21][22][23], theory of decisions [24][25][26], incremental learning [27][28][29],…”

Section: Introductionmentioning

confidence: 99%

Attribute Reduction for Information Systems via Strength of Rules and Similarity Matrix

Eid¹,

Medhat²,

Ali³

2023

Computer Systems Science and Engineering

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An information system is a type of knowledge representation, and attribute reduction is crucial in big data, machine learning, data mining, and intelligent systems. There are several ways for solving attribute reduction problems, but they all require a common categorization. The selection of features in most scientific studies is a challenge for the researcher. When working with huge datasets, selecting all available attributes is not an option because it frequently complicates the study and decreases performance. On the other side, neglecting some attributes might jeopardize data accuracy. In this case, rough set theory provides a useful approach for identifying superfluous attributes that may be ignored without sacrificing any significant information; nonetheless, investigating all available combinations of attributes will result in some problems. Furthermore, because attribute reduction is primarily a mathematical issue, technical progress in reduction is dependent on the advancement of mathematical models. Because the focus of this study is on the mathematical side of attribute reduction, we propose some methods to make a reduction for information systems according to classical rough set theory, the strength of rules and similarity matrix, we applied our proposed methods to several examples and calculate the reduction for each case. These methods expand the options of attribute reductions for researchers.

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Section: Introductionmentioning

confidence: 99%

Attribute Reduction for Information Systems via Strength of Rules and Similarity Matrix

Eid¹,

Medhat²,

Ali³

2023

Computer Systems Science and Engineering

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A comprehensive overview on demand side energy management towards smart grids: challenges, solutions, and future direction

et al. 2023

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Demand-side management, a new development in smart grid technology, has enabled communication between energy suppliers and consumers. Demand side energy management (DSM) reduces the cost of energy acquisition and the associated penalties by continuously monitoring energy use and managing appliance schedules. Demand response (DR), distributed energy resources (DER), and energy efficiency (EE) are three categories of DSM activities that are growing in popularity as a result of technological advancements in smart grids. During the last century, the energy demand has grown significantly in tandem with the increase in the global population. This is related to the expansion of business, industry, agriculture, and the increasing use of electric vehicles. Because of the sharp increase in global energy consumption, it is currently extremely difficult to manage problems such as the characterization of home appliances, integration of intermittent renewable energy sources, load categorization, various constraints, dynamic pricing, and consumer categorization. To address these issues, it is critical to examine demand-side management (DSM), which has the potential to be a practical solution in all energy demand sectors, including residential, commercial, industrial, and agricultural. This paper has provided a detailed analysis of the different challenges associated with DSM, including technical, economic, and regulatory challenges, and has proposed a range of potential solutions to overcome these challenges. The PRISMA reviewing methodology is adopted based on relevant literature to focus on the issues identified as barriers to improving DSM functioning. The optimization techniques used in the literature to address the problem of energy management were discussed, and the hybrid techniques have shown a better performance due to their faster convergence speed. Gaps in future research and prospective paths have been briefly discussed to provide a comprehensive understanding of the current DSM implementation and the potential benefits it can offer for an energy management system. This comprehensive review of DSM will assist all researchers in this field in improving energy management strategies and reducing the effects of system uncertainties, variances, and restrictions.

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Binary Representation of Polar Bear Algorithm for Feature Selection

Cited by 2 publications

References 22 publications

Attribute Reduction for Information Systems via Strength of Rules and Similarity Matrix

Attribute Reduction for Information Systems via Strength of Rules and Similarity Matrix

A comprehensive overview on demand side energy management towards smart grids: challenges, solutions, and future direction

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