Coalitions’ Weights in a Dispersed System with Pawlak Conflict Model

Abstract

The article addresses the issues related to making decisions by an ensemble of classifiers. Classifiers are built based on local tables, the set of local tables is called a dispersed knowledge. The paper discusses a novel application of Pawlak analysis model to examine the relations between classifiers and to create coalitions of classifiers. Each coalition has access to some aggregated knowledge on the basis of which joint decisions are made. Various types of coalitions are formed-a strong coalitions consisting of a large number and significant classifiers, and a weak coalitions consisting of insignificant classifiers. The new contributions of the paper is a systematical investigation of the weights of coalitions that influence the final decision. Four different method of calculating the strength of the coalitions have been applied. Each of these methods consider another aspect of the structure of the coalitions. Generally, it has been experimentally confirmed that, for a method that correctly identifies the relations between base classifiers, the use of coalitions weights improves the quality of classification. More specifically, it has been statistically confirmed that the best results are generated by the weighting method that is based on the size of the coalitions and the method based on the unambiguous of the decisions.

Rough Sets Turn 40: From Information Systems to Intelligent Systems

“…When thinking about the future it is also worth referring to the history. That reflected one more objective: acknowledging the 40th anniversary of rough sets [1], their founder [5], as well as some of relevant past and present events such as FedCSIS 2012 (rough set papers published exactly 10 years ago) [47], [48], [51], [53], [54], [57], [58], [59], [61], [64], [66], the PP-RAI 2022 rough set contest [69], [70], [72], [73], [77], [78], [81], [82], [83], [84], [89], [90], [91], [95], [96], [97], [98], [101], [105], [106] and celebration of the 30th CS&P -the event series whereby this paper's topics have been regularly addressed [36], [40], [44], [103], [116], [123].…”

“…Papers [97], [98] deal with ensembles of decision models. The first paper employs so-called Dominance-based Rough Set Balanced Rule Ensemble for fraud detection.…”

New Classification Method for Independent Data Sources Using Pawlak Conflict Model and Decision Trees

“…Pawlak’s conflict analysis model [ 28 , 29 ] is yet another approach to conflict recognition that provides excellent solutions in a variety of applications [ 30 , 31 ]. Pawlak conflict analysis model was also considered in the context of dispersed data in the papers [ 32 , 33 , 34 ]. This application shows that the Pawlak model provides excellent results for dispersed data when tables are aggregated within coalitions.…”

“…Here, the compatibility of tables is examined in terms of the information stored in them—the values on the attributes. In contrast, the papers [ 32 , 33 , 34 ] consider compatibility in terms of predictions generated by the base models created based on the tables. Another difference is that in this paper we assume that in local tables the same attributes are present, while in the papers [ 32 , 33 , 34 ] there was no such assumption.…”

“…In contrast, the papers [ 32 , 33 , 34 ] consider compatibility in terms of predictions generated by the base models created based on the tables. Another difference is that in this paper we assume that in local tables the same attributes are present, while in the papers [ 32 , 33 , 34 ] there was no such assumption. Furthermore, in this paper, the system is static, whereas previously it was dynamic.…”