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24 results on '"Greco, Salvatore"'

1. Decision Rule Approach

Author

Greco, Salvatore, Matarazzo, Benedetto, Słowiński, Roman, Price, Camille C., Series editor, Zhu, Joe, Series editor, Hillier, Frederick S., Series editor, Greco, Salvatore, editor, Ehrgott, Matthias, editor, and Figueira, José Rui, editor

Published
2016
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2. Similarity-Based Classification with Dominance-Based Decision Rules

Author

Szeląg, Marcin, Greco, Salvatore, Słowiński, Roman, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Flores, Víctor, editor, Gomide, Fernando, editor, Janusz, Andrzej, editor, Meneses, Claudio, editor, Miao, Duoqian, editor, Peters, Georg, editor, Ślęzak, Dominik, editor, Wang, Guoyin, editor, Weber, Richard, editor, and Yao, Yiyu, editor

Published
2016
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3. Label Ranking: A New Rule-Based Label Ranking Method

Author

Gurrieri, Massimo, Siebert, Xavier, Fortemps, Philippe, Greco, Salvatore, Słowiński, Roman, Greco, Salvatore, editor, Bouchon-Meunier, Bernadette, editor, Coletti, Giulianella, editor, Fedrizzi, Mario, editor, Matarazzo, Benedetto, editor, and Yager, Ronald R., editor

Published
2012
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4. Decision Rule Approach

Author

Greco, Salvatore, Matarazzo, Benedetto, Słowinński, Roman, Hillier, Frederick S., editor, Figueira, JosÉ, Greco, Salvatore, and Ehrogott, Matthias

Published
2005
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6. Dominance-Based Rough Set Approach to Preference Learning from Pairwise Comparisons in Case of Decision under Uncertainty

Author

Greco, Salvatore, Matarazzo, Benedetto, Słowiński, Roman, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Hüllermeier, Eyke, editor, Kruse, Rudolf, editor, and Hoffmann, Frank, editor

Published
2010
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7. A New Proposal for Fuzzy Rough Approximations and Gradual Decision Rule Representation

Author

Greco, Salvatore, Inuiguchi, Masahiro, Słowiński, Roman, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Peters, James F., editor, Skowron, Andrzej, editor, Dubois, Didier, editor, Grzymała-Busse, Jerzy W., editor, Inuiguchi, Masahiro, editor, and Polkowski, Lech, editor

Published
2005
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8. Multicriteria Choice and Ranking Using Decision Rules Induced from Rough Approximation of Graded Preference Relations

Author

Fortemps, Philippe, Greco, Salvatore, Słowiński, Roman, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Tsumoto, Shusaku, editor, Słowiński, Roman, editor, Komorowski, Jan, editor, and Grzymała-Busse, Jerzy W., editor

Published
2004
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9. Inducing Robust Decision Rules from Rough Approximations of a Preference Relation

Author

Slowinski, Roman, Greco, Salvatore, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Rutkowski, Leszek, editor, Siekmann, Jörg H., editor, Tadeusiewicz, Ryszard, editor, and Zadeh, Lotfi A., editor

Published
2004
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10. Rough Sets and Gradual Decision Rules

Author

Greco, Salvatore, Inuiguchi, Masahiro, Słowiński, Roman, Carbonell, J. G., editor, Siekmann, J., editor, Wang, Guoyin, editor, Liu, Qing, editor, Yao, Yiyu, editor, and Skowron, Andrzej, editor

Published
2003
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17. Inductive discovery of laws using monotonic rules

Author

BŁaszczyński, Jerzy, Greco, Salvatore, and SŁowiński, Roman

Subjects
*DATA mining, *MONOTONIC functions, *DECISION making, *CLASSIFICATION, *ROUGH sets, *A priori, *PREDICTION models, *DATA transmission systems
Abstract

Abstract: We are considering knowledge discovery from data describing a piece of real or abstract world. The patterns being induced put in evidence some laws hidden in the data. The most natural representation of patterns-laws is by “if..., then...” decision rules relating some conditions with some decisions. The same representation of patterns is used in multi-attribute classification, thus the data searched for discovery of these patterns can be seen as classification data. We adopt the classification perspective to present an original methodology of inducing general laws from data and representing them by so-called monotonic decision rules. Monotonicity concerns relationships between values of condition and decision attributes, e.g. the greater the mass (condition attribute), the greater the gravity (decision attribute), which is a specific feature of decision rules discovered from data using the Dominance-based Rough Set Approach (DRSA). While in DRSA one has to suppose a priori the presence or absence of positive or negative monotonicity relationships which hold in the whole evaluation space, in this paper, we show that DRSA can be adapted to discover rules from any kind of input classification data, exhibiting monotonicity relationships which are unknown a priori and hold in some parts of the evaluation space only. This requires a proper non-invasive transformation of the classification data, permitting representation of both positive and negative monotonicity relationships that are to be discovered by the proposed methodology. Reported results of a computational experiment confirm that the proposed methodology leads to decision rules whose predictive ability is similar to the best classification predictors. It has, however, a unique advantage over all competitors because the monotonic decision rules can be read as laws characterizing the analyzed phenomena in terms of easily understandable “if..., then...” decision rules, while other predictor models have no such straightforward interpretation. [Copyright &y& Elsevier]

Published
2012
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18. Mining Pareto-optimal rules with respect to support and confirmation or support and anti-support

Author

Brzezinska, Izabela, Greco, Salvatore, and Slowinski, Roman

Subjects
*RULE-based programming, *PARETO optimum, *DATA mining, *BAYESIAN analysis, *DECISION making, *DECISION support systems
Abstract

Abstract: In knowledge discovery and data mining many measures of interestingness have been proposed in order to measure the relevance and utility of the discovered patterns. Among these measures, an important role is played by Bayesian confirmation measures, which express in what degree a premise confirms a conclusion. In this paper, we are considering knowledge patterns in a form of “if…, then…” rules with a fixed conclusion. We investigate a monotone link between Bayesian confirmation measures, and classic dimensions being rule support and confidence. In particular, we formulate and prove conditions for monotone dependence of two confirmation measures enjoying some desirable properties on rule support and confidence. As the confidence measure is unable to identify and eliminate non-interesting rules, for which a premise does not confirm a conclusion, we propose to substitute the confidence for one of the considered confirmation measures in mining the Pareto-optimal rules. We also provide general conclusions for the monotone link between any confirmation measure enjoying the desirable properties and rule support and confidence. Finally, we propose to mine rules maximizing rule support and minimizing rule anti-support, which is the number of examples, which satisfy the premise of the rule but not its conclusion (called counter-examples of the considered rule). We prove that in this way we are able to mine all the rules maximizing any confirmation measure enjoying the desirable properties. We also prove that this Pareto-optimal set includes all the rules from the previously considered Pareto-optimal borders. [Copyright &y& Elsevier]

Published
2007
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19. Measuring expected effects of interventions based on decision rules.

Author

Greco, Salvatore, Matarazzo, Benedetto, Pappalardo, Nello, and Slowinski *, Roman

Subjects
*DECISION trees, *DECISION making, *KNOWLEDGE management, *INFORMATION resources, *INFORMATION resources management, *INDUSTRIAL efficiency
Abstract

Decision rules induced from a data set represent knowledge patterns relating premises and decisions in 'if …, then …' statements. Premise is a conjunction of elementary conditions relative to independent variables. Given a set of decision rules induced from a data set, it is useful to estimate possible effects on the dependent variables caused by an intervention on some independent variables. The authors introduce a methodology for quantifying the impact of a strategy of intervention based on a decision rule induced from data. While the usual interestingness measures of decision rules are taking into account only characteristics of universe U where they come from, the measures of efficiency of intervention depend also on characteristics of universe U' where intervention takes place. The authors are considering the intervention on a single independent variable and on a combination of these variables. [ABSTRACT FROM AUTHOR]

Published
2005
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20. Can Bayesian confirmation measures be useful for rough set decision rules?

Author

Greco, Salvatore, Pawlak, Zdzislaw, and Slowiński, Roman

Subjects
*BAYESIAN analysis, *STATISTICAL decision making, *PROBABILITY theory, *DECISION making
Abstract

Bayesian confirmation theory considers a variety of non-equivalent confirmation measures which say in what degree a piece of evidence confirms a hypothesis. In this paper, we apply some well-known confirmation measures within the rough set approach to discovering relationships in data in terms of decision rules. Moreover, we discuss some interesting properties of these confirmation measures and we propose a new property of monotonicity that is particularly relevant within rough set approach. The main result of this paper states that only two from among confirmation measures considered in the literature have the desirable properties from the viewpoint of the rough set approach. Moreover, we clarify relationships between logical implications and decision rules, and we compare the confirmation measures to several related measures, like independence (dependence) of logical formulas, interestingness measures in data mining and Bayesian solutions of raven''s paradox. [Copyright &y& Elsevier]

Published
2004
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21. EXTENSION OF THE ROUGH SET APPROACH TO MULTICRITERIA DECISION SUPPORT.

Author

Greco, Salvatore, Matarazzo, Benedetto, and Slowinski, Roman

Subjects
ROUGH sets, SET theory, MULTIPLE criteria decision making, DECISION making, APPROXIMATION theory, DECISION support systems, DECISION theory, AIRLINE industry, FINANCIAL performance
Abstract

Copyright of INFOR is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Published
2000
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22. Fuzzy rough sets and multiple-premise gradual decision rules

Author

Greco, Salvatore, Inuiguchi, Masahiro, and Slowinski, Roman

Subjects
*MATHEMATICAL logic, *INFORMATION theory, *SEARCH engines, *DATABASE searching
Abstract

Abstract: We propose a new fuzzy rough set approach which, differently from most known fuzzy set extensions of rough set theory, does not use any fuzzy logical connectives (t-norm, t-conorm, fuzzy implication). As there is no rationale for a particular choice of these connectives, avoiding this choice permits to reduce the part of arbitrary in the fuzzy rough approximation. Another advantage of the new approach is that it is based on the ordinal properties of fuzzy membership degrees only. The concepts of fuzzy lower and upper approximations are thus proposed, creating a base for induction of fuzzy decision rules having syntax and semantics of gradual rules. The proposed approach to rule induction is also interesting from the viewpoint of philosophy supporting data mining and knowledge discovery, because it is concordant with the method of concomitant variations by John Stuart Mill. The decision rules are induced from lower and upper approximations defined for positive and negative relationships between credibility degrees of multiple premises, on one hand, and conclusion, on the other hand. [Copyright &y& Elsevier]

Published
2006
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23. Induction of Ordinal Classification Rules from Incomplete Data

Author

Błaszczyński, Jerzy, Słowiński, Roman, Szeląg, Marcin, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Siekmann, Jörg, editor, Wahlster, Wolfgang, editor, Yao, JingTao, editor, Yang, Yan, editor, Słowiński, Roman, editor, Greco, Salvatore, editor, Li, Huaxiong, editor, Mitra, Sushmita, editor, and Polkowski, Lech, editor

Published
2012
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24. Developing a Decision Model for Asthma Exacerbations: Combining Rough Sets and Expert-Driven Selection of Clinical Attributes

Author

Farion, Ken, Michalowski, Wojtek, Wilk, Szymon, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Greco, Salvatore, editor, Hata, Yutaka, editor, Hirano, Shoji, editor, Inuiguchi, Masahiro, editor, Miyamoto, Sadaaki, editor, Nguyen, Hung Son, editor, and Słowiński, Roman, editor

Published
2006
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