Putting Dominance-based Rough Set Approach and robust ordinal regression together

Greco, Salvatore; Słowiński, Roman; Zielniewicz, Piotr

doi:10.1016/j.dss.2012.09.013

Cited by 55 publications

(29 citation statements)

References 26 publications

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“…Greer et al, 1994;Walker et al, 2004;Carenini and Moore, 2006). Papamichail and French (2000), Papamichail and French (2003), Bélanger and Martel (2005), Labreuche et al (2011), Labreuche et al (2012, Greco et al (2013), Sánchez-Hernández (2013) and Kadziński et al (2014) provide user-independent explanations of the model results. Both user-dependent and userindependent NLG approaches use a template-based approach (cf.…”

Section: Literature Overview On Mcda Dsss With Explanatory Functionsmentioning

confidence: 99%

A natural language generation approach to support understanding and traceability of multi-dimensional preferential sensitivity analysis in multi-criteria decision making

Wulf

Bertsch

2017

Expert Systems with Applications

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A natural language generation approach to support understanding and traceability of multi-dimensional preferential sensitivity analysis in multi-criteria decision making Abstract Multi-Criteria Decision Analysis (MCDA) enables decision makers (DM) and decision analysts (DA) to analyse and understand decision situations in a structured and formalised way. With the increasing complexity of decision support systems (DSSs), it becomes challenging for both expert and novice users to understand and interpret the model results. Natural language generation (NLG) techniques are used in various DSSs to cope with this challenge as they reduce the cognitive effort to achieve understanding of decision situations. However, NLG techniques in MCDA have so far mainly been developed for deterministic decision situations or one-dimensional sensitivity analyses. In this paper, a concept for the generation of textual explanations for a multi-dimensional preferential sensitivity analysis in MCDA is developed. The key contribution is a NLG approach that provides detailed explanations of the implications of preferential uncertainties in Multi-Attribute Value Theory (MAVT). It generates a report that assesses the influences of simultaneous or separate variations of inter-criteria and intra-criteria preferential parameters determined within the decision analysis. We explore the added value of the natural language report in an online survey. Our results show that the NLG approach is particularly beneficial for difficult interpretational tasks. IntroductionWith the aim of enabling transparent and systematic support in complex decision situations, Multi-Criteria Decision Analysis (MCDA) represents a formalised framework for the analysis of different decision alternatives (Stewart, 1992;Geldermann et al., 2009). While such decision support approaches are aimed at providing guidance to decision makers (DMs), their increasing mathematical complexity often hinders a straightforward understanding and traceability on the part of the DMs. Consequently, a lot of cognitive effort is required in order to analyse, interpret and derive adequate implications from the obtained model results which is particularly challenging for novice users (Spiegelhalter and KnillJones, 1984;Henrion and Druzdzel, 1991;Gregor and Benbasat, 1999). DMs consider such models as a 'black box', so they mistrust or even reject them (Brans and Mareschal, 1994;Bell et al., 2003), which leads to a gap between available information on the one hand and processible information on the other hand.To compensate for this, further explanations of decision analysis results promote understanding of the decision situation and thus help to increase trust and acceptability of the 3 system (Greer et al., 1994;Greef and Neerincx, 1995;Dhaliwal and Benbasat, 1996;Gregor and Benbasat, 1999;Parikh et al., 2001;Geldermann, 2010). The use of natural language generation (NLG) techniques to generate such explanations automatically based on the model results has been proposed, for instance, by Pap...

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Section: Literature Overview On Mcda Dsss With Explanatory Functionsmentioning

confidence: 99%

A natural language generation approach to support understanding and traceability of multi-dimensional preferential sensitivity analysis in multi-criteria decision making

Wulf

Bertsch

2017

Expert Systems with Applications

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“…It helps the decision maker to construct an aggregation model (preference model) by referring to the preference information provided by the decision maker [21]. Preference information can be divided into two types, direct and indirect.…”

Section: Multi-criteria Decision Analysis (Mcda)mentioning

confidence: 99%

A Recent Study on the Rough Set Theory in Multi-Criteria Decision Analysis Problems

Mohamad

Selamat

Krejcar

et al. 2015

Computational Collective Intelligence

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Abstract. Rough set theory (RST) is one of the data mining tools, which have many capabilities such as to minimize the size of an input data and to produce sets of decision rules from a set of data. RST is also one of the great techniques used in dealing with ambiguity and uncertainty of datasets. It was introduced by Z. Pawlak in 1997 and until now, there are many researchers who really make use of its advantages either to make an enhancement of the RST or to apply in various research areas such as in decision analysis, pattern recognition, machine learning, intelligent systems, inductive reasoning, data preprocessing, knowledge discovery, and expert systems. This paper presents a recent study on the elementary concepts of RST and its implementation in the multi-criteria decision analysis (MCDA) problems.

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“…Traditionally, explanations that have been constructed to accompany the use of a value function in a decision aiding process referred to performances of some alternatives on different criteria and to importance of these criteria (see, e.g., Greco et al 2013;Labreuche 2011;Labreuche et al 2012). In this paper, we propose to emphasize the relation between the original preference information and a resulting recommendation.…”

Section: Explaining Recommendation In Terms Of Decision Maker's Prefementioning

confidence: 99%

“…Without loss of generality, we focus on an additive value function preference model which is of particular interest in multiple criteria decision aiding (MCDA) because of an intuitive interpretation of numerical scores of alternatives and straightforward translation of pieces of preference information to the final result (see, e.g., Doumpos 2012;Lahdelma and Salminen 2012). Contrary to Greco et al (2013) and Labreuche et al (2012), the constructed explanations do not refer to the evaluations of alternatives but rather directly to preference information provided by the DM. Even if the required preference information is easily definable, like a set of pairwise comparisons or assignment examples, it is processed in a way preventing the DM from seeing the exact relations between the provided preference information and the obtained recommendation.…”

Section: Introductionmentioning

confidence: 99%

“…In the latter case, the sequence of preference-swaps may not be bounded. On the contrary, Greco et al (2013) applies decision rules to explain the necessary and possible preference relations. These rules aim at explaining preference relations in terms of a minimal conjunction of elementary conditions concerning evaluations on particular criteria that imply their truth.…”

Section: Introductionmentioning

confidence: 99%

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Preferential reducts and constructs in robust multiple criteria ranking and sorting

et al. 2014

Self Cite

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We revisit the multiple criteria ranking and sorting methods based on ordinal regression, which accept preference information in the form of, respectively, pairwise comparisons or assignment examples for some reference alternatives. Robust ordinal regression methods consider the whole set of value functions reproducing these holistic statements provided at the input. Its impact on the recommendation is expressed in terms of the necessary and possible preference relations or assignments. We propose methods for generating explanations of this impact, showing pieces of preference information provided by the decision maker (DM), which led to the observed outcomes. In particular, the minimal set of preference information pieces, called preferential reduct, is identified to justify some result observable for the whole set of compatible value functions (e.g., the truth of the necessary relation for some pair of alternatives). Further, the maximal set of preference information pieces, called preferential construct, is discovered to reveal the conditions under which some result non-observable for the whole set of compatible value functions (e.g., the falsity of the possible relation for some pair of alternatives) is possible. Knowing such explanations, the DM can better understand the impact of each piece of preference information on the result and, in consequence, get conviction about the obtained recommendation.

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Putting Dominance-based Rough Set Approach and robust ordinal regression together

Cited by 55 publications

References 26 publications

A natural language generation approach to support understanding and traceability of multi-dimensional preferential sensitivity analysis in multi-criteria decision making

A natural language generation approach to support understanding and traceability of multi-dimensional preferential sensitivity analysis in multi-criteria decision making

A Recent Study on the Rough Set Theory in Multi-Criteria Decision Analysis Problems

Preferential reducts and constructs in robust multiple criteria ranking and sorting

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