Neuro-fuzzy approach versus rough-set inspired methodology for intelligent decision support

Gorzałczany, Marian B.; Piasta, Zdzisław

doi:10.1016/s0020-0255(99)00070-5

Cited by 38 publications

(14 citation statements)

References 7 publications

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“…In many engineering applications it is a very important property, since it allows the transformation of data (information) into (human) knowledge. This problem has recently been faced for classiÿcation problems by Shanahan [25], Fuessel and Isermann [9], Binaghi [2], Sanchez [23], Ishibuchi [12]; in the prediction of process behavior by Maier [18]; in decision making and data mining by Gorzalczany and Piasta [10,15]; in theoretical developments on fuzzy systems by Klement [14]; in intelligent control and robotics by Stoica [26]; in function approximation, by Nauck and Kruse [19]. Jin [13] addresses interpretability by using similarity measures to check the similarity of each rule; the structure and parameters of the fuzzy rules are optimized and interpretability is improved by ÿne-tuning the fuzzy rules with regularization.…”

Section: Introductionmentioning

confidence: 99%

Interpretability and learning in neuro-fuzzy systems

Paiva

Dourado

2004

Fuzzy Sets and Systems

104

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A methodology for the development of linguistically interpretable fuzzy models from data is presented. The implementation of the model is conducted through the training of a neuro-fuzzy network, i.e., a neural net architecture capable of representing a fuzzy system. In the ÿrst phase, the structure of the model is obtained by means of subtractive clustering, which allows the extraction of a set of relevant rules based on a set of representative input-output data samples. In the second phase, the parameters of the model are tuned via the training of a neural network through backpropagation. In order to attain interpretability goals, the method proposed imposes some constraints on the tuning of the parameters and performs membership function merging. In this way, it will be easy to assign linguistic labels to each of the membership functions obtained, after training. Therefore, the model obtained for the system under analysis will be described by a set of linguistic rules, easily interpretable.

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Section: Introductionmentioning

confidence: 99%

Interpretability and learning in neuro-fuzzy systems

Paiva

Dourado

2004

Fuzzy Sets and Systems

104

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“…By combining the fuzzy sets theory and the MLP, Gorzalczany and Piasta designed a neuro-fuzzy classifier for bankruptcy prediction (Gorzalczany and Piasta 1999). The fuzzy sets-based input module allows inputting both purely numerical data as well as qualitative, linguistic data that may be used to characterize the decision-making process.…”

Section: Fuzzy Set Theory-based Techniquesmentioning

confidence: 99%

Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey

et al. 2009

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This paper presents a comprehensive review of hybrid and ensemble-based soft computing techniques applied to bankruptcy prediction. A variety of soft computing techniques are being applied to bankruptcy prediction. Our focus is on techniques, namely how different techniques are combined, but not on obtained results. Almost all authors demonstrate that the technique they propose outperforms some other methods chosen for the comparison. However, due to different data sets used by different authors and bearing in mind the fact that confidence intervals for the prediction accuracies are seldom provided, fair comparison of results obtained by different authors is hardly possible. Simulations covering a large variety of techniques and data sets are needed for a fair comparison. We call a technique hybrid if several soft computing approaches are applied in the analysis and only one predictor is used to make the final prediction. In contrast, outputs of several predictors are combined, to obtain an ensemble-based prediction.

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“…These abilities are used to support the decision making processes. There are various types of intelligent techniques that are applied in IDSS applications such as knowledge base system (Quintero et al, 2005), (Adla & Zarate, 2006), (Waiman et al, 2005), (Malhotra et al, 2003), (Palma-dos-Reis & Zahedi, 1999), (Matsatsinis & Siskos, 1999), (Linger & Burstein, 1998) and (Seder et al, 2000), data warehouse (Yu, 2004), fuzzy set theory (Liqiang et al, 2001), ANN (Sajjad & Slobodan, 2006), rough set classifier (Gorzalczany & Piasta, 1999), multi agent (Kwon et al, 2005) and etc.…”

Section: Intelligent Techniques In Idssmentioning

confidence: 99%