Journal ArticleDOI
Rough set theory: a data mining tool for semiconductor manufacturing
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TLDR
The rough set theory offers a viable approach for extraction of decision rules from data sets that can be used for making predictions in the semiconductor industry and other applications and a new rule-structuring algorithm is proposed.Abstract:
The growing volume of information poses interesting challenges and calls for tools that discover properties of data. Data mining has emerged as a discipline that contributes tools for data analysis, discovery of new knowledge, and autonomous decisionmaking. In this paper, the basic concepts of rough set theory and other aspects of data mining are introduced. The rough set theory offers a viable approach for extraction of decision rules from data sets. The extracted rules can be used for making predictions in the semiconductor industry and other applications. This contrasts other approaches such as regression analysis and neural networks where a single model is built. One of the goals of data mining is to extract meaningful knowledge. The power, generality, accuracy, and longevity of decision rules can be increased by the application of concepts from systems engineering and evolutionary computation introduced in this paper. A new rule-structuring algorithm is proposed. The concepts presented in the paper are illustrated with examples.read more
Citations
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Comparison between rough set theory and logistic regression for classifying firm's performance
TL;DR: In this paper, a comparison between rough set and logistic regression methodology is employed to identify the most significant indicators in classifying firm's performance in order to determine the financial indicators that significantly affect firm's share performance.
Book ChapterDOI
Nature Inspired Multi-Swarm Heuristics for Multi-Knowledge Extraction
TL;DR: A Multi-Swarm Synergetic Optimization (MSSO) algorithm is presented for rough set reduction and multi-knowledge extraction and empirical results illustrate that the approach can be applied for multiple reduct problems andMulti- knowledge extraction very effectively.
Journal ArticleDOI
Data mining of the GAW14 simulated data using rough set theory and tree-based methods.
TL;DR: This study proposed a two-stage process that applied rough set theory and decision trees to identify genes susceptible to the disease trait and showed that the decision trees of the first stage had accuracy rates of about 99% in predicting the disease traits.
Proceedings ArticleDOI
A data mining algorithm based on rough set theory
TL;DR: A data mining algorithm based on rough set theory (RS) is discussed, which is used to extract decision-making rule from data set and an example that uses the algorithm to acquire designing rule from the knowledge database of relay expert system is discussed.
References
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Journal ArticleDOI
Cross-Validatory Choice and Assessment of Statistical Predictions
TL;DR: In this article, a generalized form of the cross-validation criterion is applied to the choice and assessment of prediction using the data-analytic concept of a prescription, and examples used to illustrate the application are drawn from the problem areas of univariate estimation, linear regression and analysis of variance.
Journal ArticleDOI
Fundamentals of Biostatistics.
E. Barath,B. A. Rosner +1 more
TL;DR: Bernard Rosner's FUNDAMENTALS of BIOSTATISTICS is a practical introduction to the methods, techniques, and computation of statistics with human subjects that prepares students for their future courses and careers.
Book
Fundamentals of Biostatistics
TL;DR: Bernard Rosner's "Fundamentals of BIOSTATISTICS" as mentioned in this paper is a practical introduction to the methods, techniques, and computation of statistics with human subjects.