Hybrid Reduction in Soft Set Decision Making

Rose, Ahmad Nazari Mohd; Awang, Mohd Isha; Hassan, Hasni; Zakaria, Aznida Hayati; Herawan, Tutut; Deris, Mustafa Mat

doi:10.1007/978-3-642-24728-6_15

Cited by 10 publications

(8 citation statements)

References 7 publications

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“…Hence, data reduction process should be taken place before performing any decision making process, which is assisting in obtaining optimal result. On the other hand, it is important mentioning that during data reduction process, it is very crucial to ensure that the obtained reduced sets still consist the original properties and attributes of the information (Zhao et al, 2007), (Rose et al, 2012). The main objective of reduction is to lessen the number of parameters, and at the same time, attaining the property of information in helping the process of decision-making (Rose et al, 2012;Mohammed Adam et al, 2017).…”

Section: Related Workmentioning

confidence: 99%

Normal parameter reduction algorithm in soft set based on hybrid binary particle swarm and biogeography optimizer

Sadiq

Tahir

Ahmed

et al. 2019

Neural Comput & Applic

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Existing classification techniques that are proposed previously for eliminating data inconsistency could not achieve an efficient parameter reduction in soft set theory, which effects on the obtained decisions. Meanwhile, the computational cost made during combination generation process of soft sets could cause machine infinite state, which is known as Nondeterministic Polynomial time (NP). The contributions of this study are mainly focused on minimizing choices costs through adjusting the original classifications by decision partition order and enhancing the probability of searching domain space using a developed Markov chain model. Furthermore, this study introduces an efficient Soft-Set Reduction based Binary Particle Swarm optimized by Biogeography-Based Optimizer (SSR-BPSO-BBO) algorithm that generates an accurate decision for optimal and sub optimal choices. The results show that the decision partition order technique is performing better in parameter reduction up to 50%, while other algorithms could not obtain high reduction rates in some scenarios. In terms of accuracy, the proposed SSR-BPSO-BBO algorithm outperforms the other optimization algorithms in achieving high accuracy percentage of a given soft dataset. On the other hand, the proposed Markov chain model could significantly represent the robustness of our parameter reduction technique in obtaining the optimal decision and minimising the search domain.

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Section: Related Workmentioning

confidence: 99%

Normal parameter reduction algorithm in soft set based on hybrid binary particle swarm and biogeography optimizer

Sadiq

Tahir

Ahmed

et al. 2019

Neural Comput & Applic

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“…Let a soft set, study the communication prowess representation among selected university students (Rose et al, 2011).…”

Section: Soft Set Theorymentioning

confidence: 99%

Analysis of Parameterization Value Reduction of Soft Sets and Its Algorithm

Mohammed¹,

Mohd²,

Arshah³

et al. 2016

IJSECS

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In this paper, the parameterization value reduction of soft sets and its algorithm in decision making are studied and described. It is based on parameterization reduction of soft sets. The purpose of this study is to investigate the inherited disadvantages of parameterization reduction of soft sets and its algorithm. The algorithms presented in this study attempt to reduce the value of least parameters from soft set. Through the analysis, two techniques have been described. Through this study, it is found that parameterization reduction of soft sets and its algorithm has yielded a different and inconsistency in suboptimal result.

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“…Example as mentioned in [5] let "a soft set   , FE representing studies the communication prowess among selected university student using communication facilities such as "email", 2 p stands for the parameter "facebook", 3 p stands for the parameter "blog", 4 p stands for the parameter "friendsters", 5 p stands for the parameter "yahoo messenger" and lastly 6 p stands for the parameter "sms" . Consider the mapping…”

Section: Analysis Of Hybrid Reductionmentioning

confidence: 99%

“…By using this technique the false frequent object is managed and it helps to reduce the numbers of attributes, however, it increases the response time for generating the result. This paper maintains the inferior object of Hybrid reduction techniques [2]. The rest of this paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Hybrid filter for object reduction

Mohammed

Mohd

Arshah

2013

International Journal of Basic and Applied Sciences

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The basic idea to build significant attribute the uncertain objects should remove. Several theories are dealing with uncertainty, soft set theory also handles this uncertainty problem which still an open area to be explored in knowledge management. The propose techniques Known as Filtering data set which used for maintained the inferior object and we need to look at the other side of attribute reduction. The propose technique are reducing the size of object firstly, then the Hybrid reduction are executed for generating the decision extractions. These filters have reduced the size of memory without losing the characteristic of information which absolutely highly efficient. By using Filtering the inferior object of Hybrid techniques are managed. As part of this proposal, an analysis of Hybrid reduction techniques. In the conclusion part Filtering the Hybrid show better result compared to Hybrid reduction.

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Hybrid Reduction in Soft Set Decision Making

Cited by 10 publications

References 7 publications

Normal parameter reduction algorithm in soft set based on hybrid binary particle swarm and biogeography optimizer

Normal parameter reduction algorithm in soft set based on hybrid binary particle swarm and biogeography optimizer

Analysis of Parameterization Value Reduction of Soft Sets and Its Algorithm

Hybrid filter for object reduction

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