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É apresentada a explicitação do modelo matemático implícito no desenvolvimento do Sistema PSL/PSA. do Projeto 18005, em sua versão plena. E evidenciado que este modelo é um hipergrafo dirigido, categorizado, com rótulos, e além disso. que se pode representa-lo através de dígrafos categorizados com rótulos. É discutido um trabalho de Durchholz sobre representação de relações por matrizes e por Funções, no contexto de modelos matemáticos de Sistemas de Informação, que adaptam-se aos quatro enfoques abordados por esse autor.It is presented here, the explicitation of the mathematical model implicit in the developement of the PSL/PSA System of ISDDS Project, in its complete version. It is made evident that this model is a directed.categorizai hipergraph with labels. and moreover, that one can represent it by categorized digraphs with labels. It is also discussed a paper by Durchholz about reprg sentation of relations by matrixes and by functions in the context of mathema tical models of Information Systems that suit the four approaches mentioned by this author

Extending the limits of inductive machine learning through constructive and relational approaches.

Este trabalho investiga Aprendizado Indutivo de Máquina como função das linguagens de descrição, utilizadas para expressar instancias, conceitos e teoria do domínio. A ampliação do poder de representação do aprendizado proporcional e abordada no contexto de indução construtiva, no domínio de funções booleanas, com a proposta de uma estratégia de composição de atributos denominada root-fringe. Avaliações experimentais dessa e de outras estratégias de construção de novos atributos foram conduzidas e os resultados analisados. Dois métodos de poda, para tratamento de ruídos, em aprendizado de arvores de decisão, foram avaliados num ambiente de indução construtiva e os resultados discutidos. Devido a limitação do aprendizado proposicional, foram investigadas formas de ampliação dos limites do aprendizado, através da ampliação do poder representacional das linguagens de descrição. Foi escolhida Programação Lógica Indutiva - PLI - que e um paradigma de aprendizado indutivo que usa restrições de Lógica de Primeira Ordem como linguagens de descrição. O aprendizado em PLI só é factível quando as linguagens utilizadas estão restritas e é fortemente controlado, caso contrário, o aprendizado em PLI se torna indecidível. A pesquisa em PLI se direcionou a formas de restrição das linguagens de descrição da teoria do domínio e de hipóteses. Três algoritmos que \"traduzem\" a teoria do domínio de sua forma intencional, para extensional, são apresentados. As implementações de dois deles são discutidas. As implementações realizadas deram origem a dois ambientes experimentais de aprendizado: o ambiente proposicional experimental, do qual fazem parte o ambiente experimental construtivo, e o ambiente experimental relacional.This work investigates Inductive Machine Learning as a function of the description languages employed to express instances, concepts and domain theory. The enlargement of the representational power of propositional learning methods is approached via constructive induction, in the domain of boolean functions, through the proposal of a bias for composing attributes, namely, the bias root-fringe. Experimental evaluation of root-fringe, as well as other biases for constructing new attributes was conducted and the results analyzed. Two pruning methods for decision trees were evaluated in an environment of constructive induction and the results discussed. Due to the limitations of propositional learning, ways of enlarging the limits of the learning process were investigated through enlarging the representational power of the description languages. It was chosen Inductive Logic Programming - ILP - that is an inductive learning paradigm that uses restrictions of First Order Logic as description languages. Learning using ILP is only feasible when the languages are restricted and are strongly controlled; otherwise, learning in ILP becomes undecidible. Research work in ILP was directed towards restricting domain theory and hypotheses description languages. Three algorithms that \"translate\" the intentional expression of a domain theory into its extensional expression are presented. The implementations of two of them are discussed. The implementations gave rise to two experimental learning environments: the propositional environment, which includes the constructive environment, and the relational environment

Extending the limits of inductive machine learning through constructive and relational approaches.

Este trabalho investiga Aprendizado Indutivo de Máquina como função das linguagens de descrição, utilizadas para expressar instancias, conceitos e teoria do domínio. A ampliação do poder de representação do aprendizado proporcional e abordada no contexto de indução construtiva, no domínio de funções booleanas, com a proposta de uma estratégia de composição de atributos denominada root-fringe. Avaliações experimentais dessa e de outras estratégias de construção de novos atributos foram conduzidas e os resultados analisados. Dois métodos de poda, para tratamento de ruídos, em aprendizado de arvores de decisão, foram avaliados num ambiente de indução construtiva e os resultados discutidos. Devido a limitação do aprendizado proposicional, foram investigadas formas de ampliação dos limites do aprendizado, através da ampliação do poder representacional das linguagens de descrição. Foi escolhida Programação Lógica Indutiva - PLI - que e um paradigma de aprendizado indutivo que usa restrições de Lógica de Primeira Ordem como linguagens de descrição. O aprendizado em PLI só é factível quando as linguagens utilizadas estão restritas e é fortemente controlado, caso contrário, o aprendizado em PLI se torna indecidível. A pesquisa em PLI se direcionou a formas de restrição das linguagens de descrição da teoria do domínio e de hipóteses. Três algoritmos que \"traduzem\" a teoria do domínio de sua forma intencional, para extensional, são apresentados. As implementações de dois deles são discutidas. As implementações realizadas deram origem a dois ambientes experimentais de aprendizado: o ambiente proposicional experimental, do qual fazem parte o ambiente experimental construtivo, e o ambiente experimental relacional.This work investigates Inductive Machine Learning as a function of the description languages employed to express instances, concepts and domain theory. The enlargement of the representational power of propositional learning methods is approached via constructive induction, in the domain of boolean functions, through the proposal of a bias for composing attributes, namely, the bias root-fringe. Experimental evaluation of root-fringe, as well as other biases for constructing new attributes was conducted and the results analyzed. Two pruning methods for decision trees were evaluated in an environment of constructive induction and the results discussed. Due to the limitations of propositional learning, ways of enlarging the limits of the learning process were investigated through enlarging the representational power of the description languages. It was chosen Inductive Logic Programming - ILP - that is an inductive learning paradigm that uses restrictions of First Order Logic as description languages. Learning using ILP is only feasible when the languages are restricted and are strongly controlled; otherwise, learning in ILP becomes undecidible. Research work in ILP was directed towards restricting domain theory and hypotheses description languages. Three algorithms that \"translate\" the intentional expression of a domain theory into its extensional expression are presented. The implementations of two of them are discussed. The implementations gave rise to two experimental learning environments: the propositional environment, which includes the constructive environment, and the relational environment

Learning Spatial Relations Using an Inductive Logic Programming System

The ability to learn spatial relations is a prerequisite for performing many relevant tasks such as those associated with motion, orientation, navigation, etc. This paper reports on using an Inductive Logic Programmming (ILP) system for learning function-free Horn-clause descriptions of spatial knowledge. Its main contribution, however, is to show that an existing relation between two reference systems --- the speaker-relative and the absolute --- can be automatically learned by an ILP system, given the proper background knowledge and positive examples

A Constructive Fuzzy NGE Learning System

This paper presents FNGE, a learning system based on a fuzzy version of the NGE theory, describes its main modules and discusses some empirical results from its use in public domains. 2 FNGE Prototype Syste

Universidade Federal de São

Abstract. Inductive learning systems are designed to induce hypothesis, or general descriptions of concepts, from instances of these concepts. Among the wide variety of techniques used in inductive learning systems, algorithms derived from nearest neighbour (NN) pattern classification have been receiving attention lately, mainly due to their incremental nature. Nested Generalized Exemplar (NGE) theory is an inductive learning theory which can be viewed as descent from nearest neighbour classification. In NGE theory, the induced concepts take the form of hyperrectangles in a n-dimensional Euclidean space. The axes of the space are defined by the attributes used for describing the examples. This paper proposes a fuzzified version of the original NGE algorithm, which accepts input examples given as feature/fuzzy value pairs, and generalizes them as fuzzy hyperrectangles. It presents and discusses a metric for evaluating the fuzzy distance between examples, and between example and fuzzy hyperrectangles; criteria for establishing the reliability of fuzzy examples, by strengthening the exemplar which makes the right prediction and weakening the exemplar which makes a wrong one and criteria for producing fuzzy generalizations, based on the union of fuzzy sets

Enhancing classification performance using attribute-oriented functionally expanded data

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)There are many data pre-processing techniques that aim at enhancing the quality of classifiers induced by machine learning algorithms. Functional expansions (FE) are one of such techniques, which has been originally proposed to aid neural network based classification. Despite of being successfully employed, works reported in the literature use the same functional expansion, with the same expansion size (ES), applied to each attribute that describes the training data. In this paper it is argued that FE and ES can be attribute-oriented and, by choosing the most suitable FE-SE pair for each attribute, the input data representation improves and, as a consequence, learning algorithms can induce better classifiers. This paper proposes, as a pre-processing step to learning algorithms, a method that uses a genetic algorithm for searching for a suitable FE-SE pair for each data attribute, aiming at producing functionally extended training data. Experimental results using functionally expanded training sets, considering four classification algorithms, KNN, CART, SVM and RBNN, have confirmed the hypothesis; the proposed method for searching for FE-SE pairs through an attribute-oriented fashion has yielded statistically significant better results than learning from the original data or by considering the result from the best FE-SE pair for all attributes. (C) 2017 Elsevier B.V. All rights reserved.There are many data pre-processing techniques that aim at enhancing the quality of classifiers induced by machine learning algorithms. Functional expansions (FE) are one of such techniques, which has been originally proposed to aid neural network based cl893945CAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORCNPQ - CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICOCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)sem informaçãosem informaçãoThe authors thank CAPES and CNPq for the research grant received

An iterative boosting-based ensemble for streaming data classification

Among the many issues related to data stream applications, those involved in predictive tasks such as classification and regression, play a significant role in Machine Learning (ML). The so-called ensemble-based approaches have characteristics that can be appealing to data stream applications, such as easy updating and high flexibility. In spite of that, some of the current approaches consider unsuitable ways of updating the ensemble along with the continuous stream processing, such as growing it indefinitely or deleting all its base learners when trying to overcome a concept drift. Such inadequate actions interfere with two inherent characteristics of data streams namely, its possible infinite length and its need for prompt responses. In this paper, a new ensemblebased algorithm, suitable for classification tasks, is proposed. It relies on applying boosting to new batches of data aiming at maintaining the ensemble by adding a certain number of base learners, which is established as a function of the current ensemble accuracy rate. The updating mechanism enhances the model flexibility, allowing the ensemble to gather knowledge fast to quickly overcome high error rates, due to concept drift, while maintaining satisfactory results by slowing down the updating rate in stable concepts. Results comparing the proposed ensemble-based algorithm against eight other ensembles found in the literature show that the proposed algorithm is very competitive when dealing with data stream classification456678CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPSem informação#2017/00219-