A logic-based approach to similarity modeling

У овој докторској дисертацији уведен је логички приступ моделовању сличности који је заснован на интерполативној Буловој алгебри. За мерење сличности, предложене су нове интерпретабилне логичке мере, параметарске и непараметарске, као и дескриптивни оператор агегације – логичка агрегација. Поред пружања теоријске основе, у овом истраживању посебна пажња је посвећена емпиријској анализи. У сврху валидације дефинисаних мера уведена је логичка класификација заснована на ИБА сличности. За све уведене мере извршена је евалуација и поређење на реалним подацима из домена медицине, где је показано да увођење параметара унапређује резултате класификације. На крају су приказане могућности за конструисање логичких класификатора заснованих на експертским функцијама сличности на проблему предвиђања банкротства предузећа.In this doctoral thesis, a logical approach to similarity modeling based on interpolative Boolean algebra is introduced. Novel interpretable logical measures, both nonparametric and parametrized, are introduced for measuring similarity together with logical aggregation as a descriptive aggregation operator. Besides the theоretical background, in this research special attention is devoted to empirical analysis. For validation purposes, logical classification based on IBA similarity is introduced. Defined logical measures are evaluated and compared in the case of medical data, and it is shown that parameterized measures improve classification results. Finally, the benefits of logic-based classifiers with expert similarity functions are presented on the problem of corporate bankruptcy prediction

A Novel IBA-DE Hybrid Approach for Modeling Sovereign Credit Ratings

Nowadays, the sovereign credit rating is not only an index of a country’s economic performance and political stability but also an overall indicator of development and growth, as well as the trust factor that is associated with the country. Due to its importance, the vast amount of available information, and the lack of a closed-form solution, prediction models based on machine learning (ML) and computation intelligence (CI) techniques are being increasingly used to complement traditional financial approaches. In this paper, we aim to introduce a novel ML-CI approach for sovereign credit rating prediction based on a differential evolution (DE) algorithm and interpolative Boolean algebra (IBA). In fact, the proposed approach is based on a pseudo-logical function in the IBA framework derived from the historical data of publicly available indicators using the DE algorithm. Such functions are easily interpreted and enable a subtle gradation among countries. It is shown that the IBA-DE approach outperforms back-propagation neural networks on the observed problem while also providing a deeper insight into each of the indicators used for prediction and its respective influence on the prediction rating on the other

Logic-based aggregation methods for ranking student applicants

In this paper, we present logic-based aggregation models used for ranking student applicants and we compare them with a number of existing aggregation methods, each more complex than the previous one. The proposed models aim to include depen- dencies in the data using Logical aggregation (LA). LA is a aggregation method based on interpolative Boolean algebra (IBA), a consistent multi-valued realization of Boolean algebra. This technique is used for a Boolean consistent aggregation of attributes that are logically dependent. The comparison is performed in the case of student applicants for master programs at the University of Belgrade. We have shown that LA has some advantages over other presented aggregation methods. The software realization of all applied aggregation methods is also provided. This paper may be of interest not only for student ranking, but also for similar problems of ranking people e.g. employees, team members, etc

Interpolative Boolean Networks

Boolean networks are used for modeling and analysis of complex systems of interacting entities. Classical Boolean networks are binary and they are relevant for modeling systems with complex switch-like causal interactions. More descriptive power can be provided by the introduction of gradation in this model. If this is accomplished by using conventional fuzzy logics, the generalized model cannot secure the Boolean frame. Consequently, the validity of the model’s dynamics is not secured. The aim of this paper is to present the Boolean consistent generalization of Boolean networks, interpolative Boolean networks. The generalization is based on interpolative Boolean algebra, the [0,1]-valued realization of Boolean algebra. The proposed model is adaptive with respect to the nature of input variables and it offers greater descriptive power as compared with traditional models. For illustrative purposes, IBN is compared to the models based on existing real-valued approaches. Due to the complexity of the most systems to be analyzed and the characteristics of interpolative Boolean algebra, the software support is developed to provide graphical and numerical tools for complex system modeling and analysis