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Application of Rough Set Theory to Prediction of Antimicrobial Activity of Bis-Quaternary Imidazolium Chlorides

Pałkowski Ł., Krysiński J., Błaszczyński J., Słowiński R., Skrzypczak A., Błaszczak J., Gospodarek E., Wróblewska J.

Fundamenta Informaticae

2014

Vol. 132, nr 3

315--330

The paper investigates relationships between chemical structure, surface active properties and antibacterial activity of 70 bis-quaternary imidazolium chlorides. Chemical structure and properties of imidazolium chlorides were described by 7 condition attributes and antimicrobial properties were mapped by a decision attribute. Dominance-based Rough Set Approach (DRSA) was applied to discover a priori unknown rules exhibiting monotonicity relationships in the data, which hold in some parts of the evaluation space. Strong decision rules discovered in this way may enable creating prognostic models of new compounds with favorable antimicrobial properties. Moreover, relevance of the attributes estimated from the discovered rules allows to distinguish which of the structure and surface active properties describe compounds that have the most preferable and the least preferable antimicrobial properties.

A Novel Method for Elimination of Inconsistencies in Ordinal Classification with Monotonicity Constraints

Deng W., Hu F., Wang G., Błaszczyński J., Słowiński R., Szeląg M.

Fundamenta Informaticae

2013

Vol. 126, nr 4

377--395

In order to handle inconsistencies in ordinal and monotonic information systems, several relaxed versions of the Dominance-based Rough Set Approach (DRSA) have been proposed, e.g., VC-DRSA. These versions use special consistency measures to admit some inconsistent objects in the lower approximations. The minimal consistency level that has to be attained by objects included in the lower approximations is defined using a prior knowledge or a trial-and-error procedure. In order to avoid dependence on prior knowledge, an alternative way of handling inconsistencies is to iteratively eliminate the most inconsistent objects (according to some measure) until the information system becomes consistent. This idea is a base of a new method of handling inconsistencies presented in this paper and called TIPStoC. The TIPStoC algorithm is illustrated by an example from the area of telecommunication and the efficiency of the new method is proved by a computational experiment.

Ensembles of decision rules

Błaszczyński J., Dembczyński K., Kotłowski W., Słowiński R., Szeląg M.

Foundations of Computing and Decision Sciences

2006

Vol. 31, No. 3-4

221-232

In most approaches to ensemble methods, base classifiers are decision trees or decision stumps. In this paper, we consider an algorithm that generates an ensemble of decision rules that are simple classifiers in the form of logical expression: if [conditions], then [decision]. Single decision rule indicates only one of the decision classes. If an object satisfies conditions of the rule, then it is assigned to that class. Otherwise the object remains unassigned. Decision rules were common in the early machine learning approaches. The most popular decision rule induction algorithms were based on sequential covering procedure. The algorithm presented here follows a different approach to decision rule generation. It treats a single rule as a subsidiary, base classifier in the ensemble. First experimental results have shown that the presented algorithm is competitive with other methods. Additionally, generated decision rules are easy in interpretation, which is not the case of other types of base classifiers.