Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model

Miao, Duoqian; Zhao, Yan; Yao, Yiyu; Li, H. X.; Xu, Feifei

doi:10.1016/j.ins.2009.08.020

Cited by 173 publications

(86 citation statements)

References 25 publications

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“…In Bayesian approach, the model performed well for those datasets with predictor class label; however, in the absence of predictor class label for a given dataset, the Bayesian classification model assumed the record with zero probability thereby affecting the overall accuracy. On the other hand, [27] formulated a new reduct optimization method based on the condition attributes to simplify the discernibility matrix and the complexity of the attribute reduction. Analogical matrix based attributed reduction algorithm is a new approach towards attribute reduction.…”

Section: Definition 6 Reduct and Corementioning

confidence: 99%

Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique

Valarmathi

2016

CIT. J. comput. inf. technol.

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Precise rainfall forecasting is a common challenge across the globe in meteorological predictions. As rainfall forecasting involves rather complex dynamic parameters, an increasing demand for novel approaches to improve the forecasting accuracy has heightened. Recently, Rough Set Theory (RST) has attracted a wide variety of scientific applications and is extensively adopted in decision support systems. Although there are several weather prediction techniques in the existing literature, identifying significant input for modelling effective rainfall prediction is not addressed in the present mechanisms. Therefore, this investigation has examined the feasibility of using rough set based feature selection and data mining methods, namely Naïve Bayes (NB), Bayesian Logistic Regression (BLR), Multi-Layer Perceptron (MLP), J48, Classification and Regression Tree (CART), Random Forest (RF), and Support Vector Machine (SVM), to forecast rainfall. Feature selection or reduction process is a process of identifying a significant feature subset, in which the generated subset must characterize the information system as a complete feature set. This paper introduces a novel rough set based Maximum Frequency Weighted (MFW) feature reduction technique for finding an effective feature subset for modelling an efficient rainfall forecast system. The experimental analysis and the results indicate substantial improvements of prediction models when trained using the selected feature subset. CART and J48 classifiers have achieved an improved accuracy of 83.42% and 89.72%, respectively. From the experimental study, relative humidity2 (a4) and solar radiation (a6) have been identified as the effective parameters for modelling rainfall prediction.

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Section: Definition 6 Reduct and Corementioning

confidence: 99%

Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique

Valarmathi

2016

CIT. J. comput. inf. technol.

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“…Definition 7 (Pawlak 1991;Miao et al 2009) (Relative reduct of attribute set) A subset B ⊆ A is a relative reduct of A if and only if…”

Section: Definition 5 (Pawlak 1991) (Decision Table)mentioning

confidence: 99%

Attribute reduction: a horizontal data decomposition approach

Hońko

2014

Soft Comput

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Ever-growing data generate a need for new solutions to the problem of attribute reduction. Such solutions are required to deal with limited memory capacity and with many computations needed for large data processing. This paper proposes new definitions of attribute reduction using horizontal data decomposition. Algorithms for computing reducts of an information system and decision table are developed and evaluated. In the proposed approach, the size of subtables obtained during the decomposition can be arbitrarily small. The reduct sets of subtables are computed independently from one another using any heuristic method for attribute reduction. Compared with standard attribute reduction methods, the proposed approach can produce the same reducts with less space complexity and with the same or less theoretical time complexity. Experiments conducted under this work show that for information systems with fewer attributes or reducts the time needed for computing the reduct set can be shorten.

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“…This technique has led to many practical applications in various areas such as, but not limited to, medicine learning, knowledge discovery, economics, finance, engineering and even arts and culture [18,22,23,27,30,31,32,33,40,41,44]. Combined with other complementary concepts such as fuzzy sets, statistics, and logical data analysis, rough sets have been exploited in hybrid approaches to improve the performance of data analysis tools.…”

Section: Introductionmentioning

confidence: 99%

Generalization of Pawlak’s Approximations in Hypermodules by Set-Valued Homomorphisms

Mirvakili

Anvariyeh

Davvaz

2017

Foundations of Computing and Decision Sciences

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Abstract. The initiation and majority on rough sets for algebraic hyperstructures such as hypermodules over a hyperring have been concentrated on a congruence relation. The congruence relation, however, seems to restrict the application of the generalized rough set model for algebraic sets. In this paper, in order to solve this problem, we consider the concept of set-valued homomorphism for hypermodules and we give some examples of set-valued homomorphism. In this respect, we show that every homomorphism of the hypermodules is a set-valued homomorphism. The notions of generalized lower and upper approximation operators, constructed by means of a set-valued mapping, which is a generalization of the notion of lower and upper approximations of a hypermodule, are provided. We also propose the notion of generalized lower and upper approximations with respect to a subhypermodule of a hypermodule discuss some significant properties of them.

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Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model

Cited by 173 publications

References 25 publications

Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique

Identifying Effective Features and Classifiers for Short Term Rainfall Forecast Using Rough Sets Maximum Frequency Weighted Feature Reduction Technique

Attribute reduction: a horizontal data decomposition approach

Generalization of Pawlak’s Approximations in Hypermodules by Set-Valued Homomorphisms

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