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The rough set exploration system
Jan G. Bazan,Marcin Szczuka +1 more
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This article gives an overview of the Rough Set Exploration System (RSES), a freely available software system toolset for data exploration, classification support and knowledge discovery.Abstract:
This article gives an overview of the Rough Set Exploration System (RSES). RSES is a freely available software system toolset for data exploration, classification support and knowledge discovery. The main functionalities of this software system are presented along with a brief explanation of the algorithmic methods used by RSES. Many of the RSES methods have originated from rough set theory introduced by Zdzislaw Pawlak during the early 1980s.read more
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Journal ArticleDOI
Green supplier development: analytical evaluation using rough set theory
Chunguang Bai,Joseph Sarkis +1 more
TL;DR: In this paper, a rough set methodology is used to investigate the relationship between organizational attributes, supplier development program involvement attributes, and performance outcomes, focusing on environmental and business dimensions, and the methodology generates decision rules relating the various attributes to the performance outcomes.
Journal ArticleDOI
Landslide susceptibility mapping based on rough set theory and support vector machines: A case of the Three Gorges area, China
Journal ArticleDOI
Comparing Oversampling Techniques to Handle the Class Imbalance Problem: A Customer Churn Prediction Case Study
Adnan Amin,Sajid Anwar,Awais Adnan,Muhammad Nawaz,Newton Howard,Junaid Qadir,Ahmad Hawalah,Amir Hussain +7 more
TL;DR: The empirical results demonstrate that the overall predictive performance of MTDF and rules-generation based on genetic algorithms performed the best as compared with the rest of the evaluated oversampling methods and rule-generation algorithms.
Journal ArticleDOI
Customer churn prediction in the telecommunication sector using a rough set approach
TL;DR: This study proposes an intelligent rule-based decision-making technique, based on rough set theory (RST), to extract important decision rules related to customer churn and non-churn, and shows that RST based on GA is the most efficient technique for extracting implicit knowledge in the form of decision rules from the publicly available, benchmark telecom dataset.
Journal ArticleDOI
A Decision-Theoretic Rough Set Approach for Dynamic Data Mining
TL;DR: This paper presents an approach for dynamic maintenance of approximations w.r.t. objects and attributes added simultaneously under the framework of decision-theoretic rough set (DTRS) using equivalence feature vector and matrix and extensive experimental results verify the effectiveness of the proposed methods.
References
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Journal ArticleDOI
Pattern Classification and Scene Analysis.
Book
Pattern classification and scene analysis
Richard O. Duda,Peter E. Hart +1 more
TL;DR: In this article, a unified, comprehensive and up-to-date treatment of both statistical and descriptive methods for pattern recognition is provided, including Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, clustering, preprosessing of pictorial data, spatial filtering, shape description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis.
Lecture Notes in Artificial Intelligence
P. Brezillon,P. Bouquet +1 more
TL;DR: The topics in LNAI include automated reasoning, automated programming, algorithms, knowledge representation, agent-based systems, intelligent systems, expert systems, machine learning, natural-language processing, machine vision, robotics, search systems, knowledge discovery, data mining, and related programming languages.
Book
Machine Learning: Neural and Statistical Classification
TL;DR: A survey of previous comparisons and theoretical work descriptions of methods dataset descriptions criteria for comparison and methodology (including validation) empirical results machine learning on machine learning can be found in this article, where the authors also discuss their own work.