Multiple Criteria Classification Research Articles

Enumerating and categorizing positive Boolean functions separable by a [formula omitted]-additive capacity

Motivated by the elicitation or the learning of certain types of models for classifying objects in ordered categories based on several criteria, we categorize the positive Boolean functions up to 6 variables. We list all inequivalent positive Boolean functions and we determine the smallest degree k of the k-additive capacity that can be used for separating their true points from their false points. 1-additive Boolean functions are the well-studied threshold functions. Each function is described by its set of minimal true points. The latter correspond to the minimal winning coalitions of simple games. They also correspond to the minimal sufficient coalitions in the multiple criteria classification models we are interested in, namely, the MR-Sort and the noncompensatory sorting model.

A NOVEL FLOW-BASED METHOD USING GREY RELATIONAL ANALYSIS FOR PATTERN CLASSIFICATION

Flow-based methods based on the outranking relation theory are extensively used in multiple criteria classification problems. Flow-based methods usually employed an overall preference index representing the flow to measure the intensity of preference for one pattern over another pattern. A traditional flow obtained by the pairwise comparison may not be complete since it does not globally consider the differences on each criterion between all the other patterns and the latter. That is, a traditional flow merely locally considers the difference on each criterion between two patterns. In contrast with traditional flows, the relationship-based flow is newly proposed by employing the grey relational analysis to assess the flow from one pattern to another pattern by considering the differences on each criterion between all the other patterns and the latter. A genetic algorithm-based learning algorithm is designed to determine the relative weights of respective criteria to derive the overall relationship index of a pattern. Our method is tested on several real-world data sets. Its performance is comparable to that of other well-known classifiers and flow-based methods.

Rough set approach to multiple criteria classification with imprecise evaluations and assignments

Dominance-based Rough Set Approach (DRSA) has been introduced to deal with multiple criteria classification (also called multiple criteria sorting, or ordinal classification with monotonicity constraints), where assignments of objects may be inconsistent with respect to dominance principle. In this paper, we consider an extension of DRSA to the context of imprecise evaluations of objects on condition criteria and imprecise assignments of objects to decision classes. The imprecisions are given in the form of intervals of possible values. In order to solve the problem, we reformulate the dominance principle and introduce second-order rough approximations. The presented methodology preserves well-known properties of rough approximations, such as rough inclusion, complementarity, identity of boundaries and precisiation. Moreover, the meaning of the precisiation property is extended to the considered case. The paper presents also a way to reduce decision tables and to induce decision rules from rough approximations.

Objective classification of empirical probability distributions and the issue of event detection

The paper concentrates on the issue of classification of empirical probability distributions (histograms), which is useful both in management situations and in event detection or event mining. While existing approaches to event detection concentrate on the use of selected moments or other characteristics of empirical probability distributions, we postulate that full empirical distribution preserves more of needed information then selected moments of this distribution, thus multiple criteria classification of distributions can be most effective in event detection.

Web-Integration PROAFTN Methodology for Acute Leukemia Diagnosis

To develop and test a web-based Clinical Decision Support System (CDSS) tool, which integrated a new fuzzy multiple criteria classification methodology named PROAFTN in acute leukemia (AL) diagnosis. We have integrated a PROAFTN method and developed a web-based clinical decision support system using standard JSP, servlets, and XML technologies. All website data are database-driven; and the database system can handle data store, update, and retrieval instantly. Since the system was moved to a web server, we have started our experimental testing on 191 AL cases. The percentage of correct classification in this experimental testing was consistent with the proposed prototype. 96.4% of AL cases were correctly classified, proving that web-integration can be a promising tool for dissemination of CDSS tools. We found our system to be robust and capable of deployment for referring physicians. Our experimental results suggest that the Internet has promise as a means for distribution of CDSS tools. This system will help to: 1) make a "virtual" diagnosis and to compare its performances with given clinical diagnosis; 2) exchange health information between physicians and hematologists at the location and time of need; 3) assist online learning and simulate cases for training practitioners; 4) implement a strict security and access control for transmission of electronic health data through the Internet. The method will not replace specialists, but was developed to assist biologist-hematologists and general practitioners remotely in making decisions on medical diagnosis.

Learning multicriteria fuzzy classification method PROAFTN from data

In this paper, we present a new methodology for learning parameters of multiple criteria classification method PROAFTN from data. There are numerous representations and techniques available for data mining, for example decision trees, rule bases, artificial neural networks, density estimation, regression and clustering. The PROAFTN method constitutes another approach for data mining. It belongs to the class of supervised learning algorithms and assigns membership degree of the alternatives to the classes. The PROAFTN method requires the elicitation of its parameters for the purpose of classification. Therefore, we need an automatic method that helps us to establish these parameters from the given data with minimum classification errors. Here, we propose variable neighborhood search metaheuristic for getting these parameters. The performances of the newly proposed method were evaluated using 10 cross validation technique. The results are compared with those obtained by other classification methods previously reported on the same data. It appears that the solutions of substantially better quality are obtained with proposed method than with these former ones.

Multiple criteria classification with an application in water resources planning

A new kind of multiple criteria decision aid (MCDA) problem, multiple criteria classification (MCC), is studied in this paper. Traditional classification methods in MCDA focus on sorting alternatives into groups ordered by preference. MCC is the classification of alternatives into nominal groups, structured by the decision maker (DM), who specifies multiple characteristics for each group. Starting with illustrative examples, the features, definition and structures of MCC are presented, emphasizing criterion and alternative flexibility. Then an analysis procedure is proposed to solve MCC problems systematically. Assuming additive value functions, an optimization model with constraints that incorporate various classification strategies is constructed to solve MCC problems. An application of MCC in water resources planning is carried out and some future extensions are suggested.

Correction of a correctional psychologist in treatment of the criminal offender : by Harold F. Uehling Charles C. Thomas (301–327 East Lawrence Avenue, Springfield, Illinois 62703), 1973, 213 pp., hardcover—$11.95

Motivated by the elicitation or the learning of certain types of models for classifying objects in ordered categories based on several criteria, we categorize the positive Boolean functions up to 6 variables. We list all inequivalent positive Boolean functions and we determine the smallest degree k of the k-additive capacity that can be used for separating their true points from their false points. 1-additive Boolean functions are the well-studied threshold functions. Each function is described by its set of minimal true points. The latter correspond to the minimal winning coalitions of simple games. They also correspond to the minimal sufficient coalitions in the multiple criteria classification models we are interested in, namely, the MR-Sort and the noncompensatory sorting model.