An Alternative Algorithm for Soft Set Parameter Selection Using Special Order

Autor:	Wan Maseri Binti Wan Mohd, Mohammed Adam Taheir Mohammed, Edi Sutoyo, Haruna Chiroma, Mungad Mungad, Ruzaini Abdullah Arshah
Rok vydání:	2019
Předmět:	Reduction (complexity) Set (abstract data type) Mathematical optimization Computer science Population-based incremental learning Process (computing) Partition (database) Algorithm Selection (genetic algorithm) Soft set FSA-Red Algorithm
Zdroj:	Proceedings of the International Conference on Data Engineering 2015 (DaEng-2015) ISBN: 9789811317972 DaEng
DOI:	10.1007/978-981-13-1799-6_23
Popis:	The outcome of the reduction of soft data is dependent on the quality and discount evidence that increases with optimization analysis. There is a set of techniques that can be used to reduce the data, but the different techniques showed different results as each technique is focused on solving a particular problem. This paper proposed a parameter reduction algorithm, known as 3C algorithm, to circumvent the false frequent object in reduction. Results indicated that the proposed algorithm is easy to implement and perform better than the state-of-the-art parameter reduction algorithm. Also, the proposed algorithm can be used as an effective alternative method for reducing parameters in order to enhance the decision-making process based on decision partition order. Comparative analysis were performed between the proposed algorithm and the state-of-the-art parameter reduction algorithm using several soft set in terms of parameter reduction.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::ae40a3d0c8b34e509a4c6775d62905a7 https://doi.org/10.1007/978-981-13-1799-6_23 Zobrazit plný text záznamu