Multicriteria classification and sorting methods: A literature review

Zopounidis, Constantin; Doumpos, Michael

doi:10.1016/s0377-2217(01)00243-0

Cited by 561 publications

(283 citation statements)

References 94 publications

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“…On the other hand, an instance ranking in PL-ML is about ordering a set of instances according to their (unknown) preference degrees (see, e.g., Waegeman and De Baets 2011). This, in turn, is equivalent to a definition of a multiple criteria sorting problem (Zopounidis and Doumpos 2002). Let us formulate the problem that is considered in Robust Ordinal Regression (its detailed explanation is provided subsequently along with the comparative reference to PL-ML):…”

Section: Problem Formulationmentioning

confidence: 99%

Robust ordinal regression in preference learning and ranking

et al. 2013

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Multiple Criteria Decision Aiding (MCDA) offers a diversity of approaches designed for providing the decision maker (DM) with a recommendation concerning a set of alternatives (items, actions) evaluated from multiple points of view, called criteria. This paper aims at drawing attention of the Machine Learning (ML) community upon recent advances in a representative MCDA methodology, called Robust Ordinal Regression (ROR). ROR learns by examples in order to rank a set of alternatives, thus considering a similar problem as Preference Learning (ML-PL) does. However, ROR implements the interactive preference construction paradigm, which should be perceived as a mutual learning of the model and the DM. The paper clarifies the specific interpretation of the concept of preference learning adopted in ROR and MCDA, comparing it to the usual concept of preference learning considered within ML. This comparison concerns a structure of the considered problem, types of admitted preference information, a character of the employed preference models, ways of exploiting them, and techniques to arrive at a final ranking.

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Section: Problem Formulationmentioning

confidence: 99%

Robust ordinal regression in preference learning and ranking

et al. 2013

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“…in [17], many of the optimization problems encountered fall within the area of (smooth) Convex Programming. However, other areas of Mathematical Optimization play a notable role, among others, Global Optimization [9,13,51,128,160,245], Linear Programming [94,158,205] Mixed-Integer Programming [25,39,50,77,220,228], Nonsmooth Optimization [7,13,44,51,222,223], Multicriteria and Multi-Objective Programming [68,93,181,248] and Robust Optimization [224].…”

Section: Introductionmentioning

confidence: 99%

Supervised classification and mathematical optimization

Carrizosa

Morales

2013

Computers & Operations Research

124

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Data Mining techniques often ask for the resolution of optimization problems. Supervised Classification, and, in particular, Support Vector Machines, can be seen as a paradigmatic instance. In this paper, some links between Mathematical Optimization methods and Supervised Classification are emphasized. It is shown that many different areas of Mathematical Optimization play a central role in off-the-shelf Supervised Classification methods. Moreover, Mathematical Optimization turns out to be extremely useful to address important issues in Classification, such as identifying relevant variables, improving the interpretability of classifiers or dealing with vagueness/noise in the data.

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“…In recent years, the most common approaches to address the classification problem have been from the fields of Artificial Intelligence/machine learning [35,50] and Multi-Criteria Decision Aid (MCDA) [39,54]. Machine learning algorithms are designed to learn a function which maps a large vector of attributes into one of several classes.…”

Section: Introductionmentioning

confidence: 99%

“…nominal sorting problems). In MCDA, the problem of assigning objects to predefined classes is known as a multiple criteria classification problem (MCCP) [54]. The decision problems in MCCP require a comparison between alternatives or objects based on the scores of attributes using absolute evaluations [32].…”

Section: Introductionmentioning

confidence: 99%

An evolutionary framework using particle swarm optimization for classification method PROAFTN

Al-Obeidat¹,

Belacel²,

Carretero³

et al. 2011

Applied Soft Computing

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The aim of this paper is to introduce a methodology based on the particle swarm optimization (PSO) algorithm to train the Multi-Criteria Decision Aid (MCDA) method PROAFTN. PSO is an efficient evolutionary optimization algorithm using the social behavior of living organisms to explore the search space. It is a relatively new population-based metaheuristic that can be used to find approximate solutions to difficult optimization problems. Furthermore, it is easy to code and robust to control parameters. To apply PROAFTN, the values of several parameters need to be determined prior to classification, such as boundaries of intervals and weights. In this study, the proposed technique is named PSOPRO, which utilizes PSO to elicit the PROAFTN parameters from examples during the learning process. To test the effectiveness of the methodology and the quality of the obtained models, PSOPRO is evaluated on 12 public-domain datasets and compared with the previous work applied on PROAFTN. The computational results demonstrate that PSOPRO is very competitive with respect to the most common classification algorithms.

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Multicriteria classification and sorting methods: A literature review

Cited by 561 publications

References 94 publications

Robust ordinal regression in preference learning and ranking

Robust ordinal regression in preference learning and ranking

Supervised classification and mathematical optimization

An evolutionary framework using particle swarm optimization for classification method PROAFTN

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