Aprendizado supervisionado usando redes neurais construtivas.

Constructive neural learning is a neural learning model that does not assume a fixed network topology before training begins. The main characteristic of this learning model is the dynamic construction of the network s hidden layers that occurs simultaneously with training. This work investigates three topics related to constructive neural learning namely algorithms for training an individual TLU, constructive neural algorithms for two class problems and constructive neural algorithms for multiclass problems. The first research topic is approached by discussing a few TLU training algorithms, namely Perceptron, Pocket, Thermal, Modified Thermal, MinOver and BCP. This work approaches constructive neural learning for two class classification tasks by initially reviewing Tower, Pyramid, Tiling and Upstart algorithms, aiming at their multiclass versions. Next five constructive neural algorithms namely Shift, Offset, PTI, Perceptron Cascade and Sequential are investigated and two hybrid algorithms are proposed: Hybrid Tiling, that does not restrict the TLU s training to only one algorithm and the OffTiling, a collaborative approach based on Tiling and Offset. Multiclass constructive neural learning was approached by investigating TLUs training algorithms that deal with multiclass as well as by investigating multiclass versions of Tower, Pyramid, Tiling, Upstart and Perceptron Cascade. This research work also describes an empirical evaluation of all the investigated algorithms conducted using several knowledge domains. Results are discussed and analyzed.Financiadora de Estudos e ProjetosAprendizado neural construtivo é um modelo de aprendizado neural que não pressupõe a definição de uma topologia de rede fixada antes do início do treinamento. A principal característica deste modelo de aprendizado é a construção dinâmica das camadas intermediárias da rede, à medida que vão sendo necessárias ao seu treinamento. Este trabalho investiga três frentes de pesquisas com relação ao aprendizado neural construtivo, a saber, algoritmos para o treinamento de TLUs, algoritmos neurais construtivos para problemas que envolvem duas classes e algoritmos neurais construtivos para o tratamento de problemas multiclasses. Com relação à primeira frente de pesquisa os algoritmos discutidos para o treinamento de TLUs são o Perceptron, o Pocket, o PMR, o Thermal, o Thermal Modificado, o MinOver e o BPC. Na frente de pesquisa relativa ao aprendizado neural construtivo para duas classes são revistos os algoritmos Tower, Pyramid, Tiling e Upstart, para que as versões multiclasses desses algoritmos possam ser tratadas. São investigados os algoritmos neurais construtivos Shift, Offset, PTI, Perceptron Cascade e Sequential e propostos dois algoritmos híbridos: o Tiling Híbrido, que não restringe o treinamento de TLUs a um único algoritmo e o OffTiling que agrega os algoritmos Tiling e Offset. A frente que focaliza o aprendizado neural construtivo multiclasse investiga os algoritmos para o treinamento de TLUs quando o problema envolvido apresentar mais que duas classes bem como apresenta e discute as versões multiclasses dos algoritmos Tower, Pyramid, Tiling, Upstart e Perceptron Cascade. O trabalho descreve uma avaliação empírica dos algoritmos investigados, em vários domínios de conhecimento bem como discute e analisa os resultados obtidos

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