Distance and Similarity Measures for Soft Sets

In [P. Majumdar, S. K. Samanta, Similarity measure of soft sets, New Mathematics and Natural Computation 4(1)(2008) 1-12], the authors use matrix representation based distances of soft sets to introduce matching function and distance based similarity measures. We first give counterexamples to show that their Definition 2.7 and Lemma 3.5(3) contain errors, then improve their Lemma 4.4 making it a corllary of our result. The fundamental assumption of Majumdar et al has been shown to be flawed. This motivates us to introduce set operations based measures. We present a case (Example 28) where Majumdar-Samanta similarity measure produces an erroneous result but the measure proposed herein decides correctly. Several properties of the new measures have been presented and finally the new similarity measures have been applied to the problem of financial diagnosis of firms.Comment: 14 pages, accepted manuscript, to appear in New Mathematics and Natural Computatio

CBR and MBR techniques: review for an application in the emergencies domain

The purpose of this document is to provide an in-depth analysis of current reasoning engine practice and the integration strategies of Case Based Reasoning and Model Based Reasoning that will be used in the design and development of the RIMSAT system. RIMSAT (Remote Intelligent Management Support and Training) is a European Commission funded project designed to: a.. Provide an innovative, 'intelligent', knowledge based solution aimed at improving the quality of critical decisions b.. Enhance the competencies and responsiveness of individuals and organisations involved in highly complex, safety critical incidents - irrespective of their location. In other words, RIMSAT aims to design and implement a decision support system that using Case Base Reasoning as well as Model Base Reasoning technology is applied in the management of emergency situations. This document is part of a deliverable for RIMSAT project, and although it has been done in close contact with the requirements of the project, it provides an overview wide enough for providing a state of the art in integration strategies between CBR and MBR technologies.Postprint (published version

Non classical concept representation and reasoning in formal ontologies

Formal ontologies are nowadays widely considered a standard tool for knowledge representation and reasoning in the Semantic Web. In this context, they are expected to play an important role in helping automated processes to access information. Namely: they are expected to provide a formal structure able to explicate the relationships between different concepts/terms, thus allowing intelligent agents to interpret, correctly, the semantics of the web resources improving the performances of the search technologies. Here we take into account a problem regarding Knowledge Representation in general, and ontology based representations in particular; namely: the fact that knowledge modeling seems to be constrained between conflicting requirements, such as compositionality, on the one hand and the need to represent prototypical information on the other. In particular, most common sense concepts seem not to be captured by the stringent semantics expressed by such formalisms as, for example, Description Logics (which are the formalisms on which the ontology languages have been built). The aim of this work is to analyse this problem, suggesting a possible solution suitable for formal ontologies and semantic web representations. The questions guiding this research, in fact, have been: is it possible to provide a formal representational framework which, for the same concept, combines both the classical modelling view (accounting for compositional information) and defeasible, prototypical knowledge ? Is it possible to propose a modelling architecture able to provide different type of reasoning (e.g. classical deductive reasoning for the compositional component and a non monotonic reasoning for the prototypical one)? We suggest a possible answer to these questions proposing a modelling framework able to represent, within the semantic web languages, a multilevel representation of conceptual information, integrating both classical and non classical (typicality based) information. Within this framework we hypothesise, at least in principle, the coexistence of multiple reasoning processes involving the different levels of representation

A new method for feature selection based on fuzzy similarity measures using multi objective genetic algorithm

Feature selection (FS) is considered to be an important preprocessing step in machine learning and pattern recognition, and feature evaluation is the key issue for constructing a feature selection algorithm. Feature selection process can also reduce noise and this way enhance the classification accuracy. In this article, feature selection method based on fuzzy similarity measures by multi objective genetic algorithm (FSFSM - MOGA) is introduced and performance of the proposed method on published data sets from UCI was evaluated. The results show the efficiency of the method is compared with the conventional version. When this method multi-objective genetic algorithms and fuzzy similarity measures used in CFS method can improve it

Kreiranje lozinki: između prototipne perpektive i konceptualnog prostora LJUBAVI

Aided by the instruments of prototype theory, the current study sets out to determine whether in password creation there is a common underlying cognitive pattern in the categorization of the elusive natural language concept of LOVE. Our framework combines free listing, a method providing critical information about the words that are more generally associated with a concept, and analysis of prototype rating surveys. The results obtained are compared to a dataset of randomly selected passwords to determine the semantic associations of the concept of LOVE and clarify the semantic processes involved in the structure of passwords. Results suggest that, in categorizing LOVE, password users have compatible representations that afford a meeting of minds. We conclude that LOVE acts as a fixpoint in the mental processing of this CONCEPTUAL SPACE and that it takes, with password users, idealized forms of representations rather than individual experience-based representations, as might be expected. Our investigation method has facilitated the collection of data on how LOVE prototypes specify more exhaustively the mode of synthesis and the cognitive mapping under which these may occur.Primjenjujući instrumente teorije prototipova, u ovoj se studiji nastoji utvrditi postoji li pri kreiranju lozinki zajednički kognitivni uzorak na temelju kojega se kategorizira pojam LJUBAVI. Analitičku okosnicu rada čini kombinacija metode slobodnog nabrajanja, kojom se izlučilo ključne informacije o riječima koje se općenito vezuju uz pojam i analize rezultata rangiranja prototipnosti. Dobiveni su rezultati uspoređeni s bazom nasumično odabranih lozinki kako bi se utvrdile semantičke asocijacije pojma LJUBAVI i razjasnili semantički procesi u strukturi lozinki. Rezultati ukazuju na to da u kategorizaciji LJUBAVI korisnici lozinki imaju sukladne konceptualne prikaze koji omogućuju susret umova. Zaključak je studije da LJUBAV djeluje kao svojevrsno sidršte u mentalnoj obradi dotičnog KONCEPTUALNOG PROSTORA te da ga u korisnika lozinki obilježava idealiziran oblik prikaza, a ne, protivno očekivanjima, prikazi temeljeni na pojedinačnim iskustvima. Naš je analitički pristup omogućio prikupljanje podataka o tome na koji način prototipovi LJUBAVI elaboriraju način sintetiziranja i kognitivnih preslikavanja unutar kojih se oni mogu pojaviti