Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees

This paper investigates an important problem in stream mining, i.e., classification under streaming emerging new classes or SENC. The common approach is to treat it as a classification problem and solve it using either a supervised learner or a semi-supervised learner. We propose an alternative approach by using unsupervised learning as the basis to solve this problem. The SENC problem can be decomposed into three sub problems: detecting emerging new classes, classifying for known classes, and updating models to enable classification of instances of the new class and detection of more emerging new classes. The proposed method employs completely random trees which have been shown to work well in unsupervised learning and supervised learning independently in the literature. This is the first time, as far as we know, that completely random trees are used as a single common core to solve all three sub problems: unsupervised learning, supervised learning and model update in data streams. We show that the proposed unsupervised-learning-focused method often achieves significantly better outcomes than existing classification-focused methods

Performance Evaluation of Non Functional Requirements

Requirement engineering (RE) concerns goal identification by a system, operationalization of such goals into services and constraints, and assigning responsibilities, needs to agents including humans, devices/software. RE processes include negotiation, documentation, domain analysis, specification, elicitation, assessment, and evolution. It is difficult and critical to get high quality requirements. The paper gives a synopsis of the field of requirements engineering. RE is defined, and a brief history of main concepts and techniques is presented. The result got by using the method is very promising. It was evaluated extensively on Non Functional Requirements (NFR) dataset obtained from PROMISE repository, which is publicly accessible

METODOLOGIA DE IDENTIFICAÇÃO DE CÉDULAS MONETÁRIAS PARA DEFICIENTES VISUAIS

Worldwide, there is a wide variety of disabled people. It is estimated that a total of 285 million visually impaired people, 39 million blind and 246 million with low vision. The use of Information and Communication Technologies helps disabled people to have independence that is more significant, quality of life and inclusion in social life through the supplementation, maintenance or devolution of their functional capacities. In this context, this paper presents a methodology for banknotes automatic identification that can be widely used by the visually impaired people. For this, a set of four local descriptors, four individual classifiers and two classifier committees were evaluated, which could be used as a basis for the development of applications for the identification of banknotes. The tests performed with images of dollar, euro and real banknotes obtained precision rates of 97%, 91% and 91%, respectively.Mundialmente, existe uma grande variedade de deficientes. Estima-se um total de 285 milhões de deficientes visuais, sendo 39 milhões cegos e 246 milhões com baixa visão. O uso de Tecnologias de Informação e Comunicação contribui para que deficientes tenham maior independência, qualidade de vida e inclusão na vida social por meio do suplemento, manutenção ou devolução de suas capacidades funcionais. Nesse contexto, este artigo apresenta uma metodologia de identificação automática de cédulas monetárias que poderá ser amplamente utilizada por deficientes visuais. Para isso, foram avaliados um conjunto de quatro descritores locais, quatro classificadores individuais e dois comitês de classificadores, que poderão ser utilizados como base para o desenvolvimento de aplicativos para a identificação de cédulas. Nos testes realizados, obteve-se valores de precisão de 97%, 91% e 91% em cédulas de dólar, euro e real, respectivamente

Improving deep forest by confidence screening

Most studies about deep learning are based on neural network models, where many layers of parameterized nonlinear differentiable modules are trained by backpropagation. Recently, it has been shown that deep learning can also be realized by non-differentiable modules without backpropagation training called deep forest. The developed representation learning process is based on a cascade of cascades of decision tree forests, where the high memory requirement and the high time cost inhibit the training of large models. In this paper, we propose a simple yet effective approach to improve the efficiency of deep forest. The key idea is to pass the instances with high confidence directly to the final stage rather than passing through all the levels. We also provide a theoretical analysis suggesting a means to vary the model complexity from low to high as the level increases in the cascade, which further reduces the memory requirement and time cost. Our experiments show that the proposed approach achieves highly competitive predictive performance with significantly reduced time cost and memory requirement by up to one order of magnitude