Rough sets in the Soft Computing environment

Bello, Rafael; Verdegay, José L.

doi:10.1016/j.ins.2012.04.041

Cited by 47 publications

(23 citation statements)

References 88 publications

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“…Several studies have focused on the relationship between rough set theory and the DS-based evidential reasoning [44,45]. These studies show that belief (plausibility) functions can be derived from a classic Pawlak rough set.…”

Section: Interpretation Of Evidential Reasoning On Fuzzy Rough Setmentioning

confidence: 99%

Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City

Yang

et al. 2016

Remote Sensing

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Urban fringe is the transition zone fine grained with urban and non-urban land cover types. The complex landscape mosaic in this area challenges the land cover classification based on the remote-sensing data. Spectral signatures are not efficient to discriminate all pixels into classes. To improve the recognition and handle the uncertainty, this paper provides a novel integrated approach, based on a fuzzy rough set and evidential reasoning (FRSER), for land cover classification in an urban fringe area. The approach is implemented on Landsat Operation Land Imager data covering the urban fringe area of Wuhan city, China. A fuzzy rough set is first used to define a decision table from multispectral imagery and ground reference data. Then the fuzzy rough information system is interpreted using the Dempster-Shafer theory, based on an evidential reasoning system. A final land cover classification with uncertainty is achieved by evidential reasoning. The results are compared with the traditional maximum likelihood classifier (MLC) and some rough set-based classifiers including classical rough set classifier (RS), fuzzy rough set classifier (FRS), and variable precision fuzzy rough set classifier (VPFRS). The better overall accuracy, user's and producer's accuracies, and the kappa coefficient, in comparison with the other classifiers, suggest that the proposed approach can effectively discriminate land cover types in urban fringe areas with high inter-class similarities and intra-class heterogeneity. It is also capable of handling the uncertainty in data processing, and the final land cover map comes with a degree of uncertainty. The proposed approach that can efficiently integrate the merits of both the fuzzy rough set and DS theory provides an efficient method for urban fringe land cover classification.

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Section: Interpretation Of Evidential Reasoning On Fuzzy Rough Setmentioning

confidence: 99%

Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City

Yang

et al. 2016

Remote Sensing

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“…There is a move toward data-driven knowledge discovery and decision-making. Data reduction is an important step in knowledge discovery from large datasets [1] [2]. The high dimensionality of dataset can be reduced by using suitable techniques, depending on the requirements of the data mining approach.…”

Section: Introductionmentioning

confidence: 99%

“…The techniques fall into one of the two categories: those that transform the underlying meaning of the data features and those that are semantics-preserving [3] [4]. Feature selection (FS) methods belong to the latter category, where a smaller set of the original features is chosen based on a subset evaluation criteria [2]. Rough set theory is an efficient mathematical technique for knowledge discovery from datasets [1].…”

Section: Introductionmentioning

confidence: 99%

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An Enhanced Rough Set based Feature Grouping Approach for Supervised Feature Selection

Bania¹

2018

IJMSC

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Selection of useful information from a large data collection is an important and challenging problem. Feature selection refers to the problem of selecting relevant features from a given dataset which produces the most predictive outcome as the original features maintain before the selection. Rough set theory (RST) and its extension are the most successful mathematical tools for feature selection from a given dataset. This paper starts with an outline of the fundamental concepts behind the rough set and fuzzy rough set based feature grouping techniques which are related to supervise feature selection. Supervised Quickreduct (QR) and fuzzyrough feature grouping Quickreduct (FQR) algorithms are highlighted here. Then an enhanced version of FQR method is proposed here which is based on rough set dependency criteria with feature significance measure that select a minimal subset of features. Also, the termination condition of the base method is modified. Experimental studies of the algorithms are carried out on five public domain benchmark datasets available in UCI machine learning repository. JRip and J48 classifier are used to measure the classification accuracy. The performance of the proposed method is found to be satisfactory in comparison with other methods.

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“…However, methods using fuzzy sets require parameters of the membership functions to be tuned and eventually some preprocessing of the input data to be done if pertinent input variables need to be identified. Moreover, when using rough sets one needs to process the input data in order to deal with the indiscernibility relation and establish upper and lower approximations of the concepts pertaining the problem, see (Bello & Verdegay, 2012). Finally, application of the Dempster-Shafer (evidence) theory is a matter of subjective estimation of uncertainty as it assumes that values of belief (or plausibility) are given by an expert.…”

Section: Introductionmentioning

confidence: 99%

Solving the linear interval tolerance problem for weight initialization of neural networks

Adam¹,

Karras²,

Magoulas³

et al. 2014

Neural Networks

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Determining good initial conditions for an algorithm used to train a neural network is considered a parameter estimation problem dealing with uncertainty about the initial weights. Interval Analysis approaches model uncertainty in parameter estimation problems using intervals and formulating tolerance problems. Solving a tolerance problem is defining lower and upper bounds of the intervals so that the system functionality is guaranteed within predefined limits. The aim of this paper is to show how the problem of determining the initial weight intervals of a neural network can be defined in terms of solving a linear interval tolerance problem. The proposed Linear Interval Tolerance Approach copes with uncertainty about the initial weights without any previous knowledge or specific assumptions on the input data as required by approaches such as fuzzy sets or rough sets. The proposed method is tested on a number of well known benchmarks for neural networks trained with the back-propagation family of algorithms. Its efficiency is evaluated with regards to standard performance measures and the results obtained are compared against results of a number of well known and established initialization methods. These results provide credible evidence that the proposed method outperforms classical weight initialization methods.

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Rough sets in the Soft Computing environment

Cited by 47 publications

References 88 publications

Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City

Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City

An Enhanced Rough Set based Feature Grouping Approach for Supervised Feature Selection

Solving the linear interval tolerance problem for weight initialization of neural networks

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