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Abstract: In this study, we introduce a design methodology of polynomial function-based Neural Network (pf-NN) classifiers (predictors). The essential design components include Fuzzy C-Means (FCM) regarded as a generic clustering algorithm and polynomials providing all required nonlinear capabilities of the model. The learning method uses a weighted cost function (objective function) while to analyze the performance of the system we engage a standard receiver operating characteristics (ROC) analysis. The proposed networks are used to detect software defects. From the conceptual standpoint, the classifier of this form can be expressed as a collection of “if-then” rules. Fuzzy clustering (Fuzzy C-Means, FCM) is aimed at the development of premise layer of the rules while the corresponding consequences of the rules are formed by some local polynomials. A detailed learning algorithm for the pf-NNs is presented with particular provisions made for dealing with imbalanced classes encountered quite commonly in software quality problems. The use of simple measures such as accuracy of classification becomes questionable. In the assessment of quality of classifiers, we confine ourselves to the use of the area under curve (AUC) in the receiver operating characteristics (ROCs) analysis. AUC comes as a sound classifier metric capturing a tradeoff between the high true positive rate (TP) and the low false positive rate (FP). The performance of the proposed classifier is contrasted with the results produced by some “standard” Radial Basis Function (RBF) neural networks. [Copyright &y& Elsevier]

Label distribution learning (LDL) is a generalized machine learning framework for dealing with label ambiguity, as it can explore the relative importance levels of different labels in the description of each sample. Although several algorithms have been proposed to solve LDL problems, they usually destroy the consistency of geometric structures between feature space and label space to a certain extent, which frequently plays a significant role in learning tasks. Meanwhile, most existing LDL algorithms only take predictive performances into consideration, while ignoring the computational cost and robustness to noises. To remedy above deficiencies, we propose a novel algorithm, i.e., Local Collaborative Representation based Label Distribution Learning, shortly LCR-LDL. In LCR-LDL, an unlabeled sample is treated as the collaborative representation of the local dictionary constructed by the neighborhood of the unlabeled sample, and the discriminatory information of representation coefficients is used to reconstruct the label distribution of the unlabeled sample. Experimental results on 20 real-world LDL data sets compared with results produced by 11 state-of-the-art algorithms show that, the proposed LCR-LDL algorithm can not only effectively improve the predictive performances for LDL tasks, but also exhibit higher robustness and a lightweight computational overhead. This study suggests new trends for considering the computational cost and robustness issues in the LDL community. [ABSTRACT FROM AUTHOR]

Abstract: Kernel methods and rough sets are two general pursuits in the domain of machine learning and intelligent systems. Kernel methods map data into a higher dimensional feature space, where the resulting structure of the classification task is linearly separable; while rough sets granulate the universe with the use of relations and employ the induced knowledge granules to approximate arbitrary concepts existing in the problem at hand. Although it seems there is no connection between these two methodologies, both kernel methods and rough sets explicitly or implicitly dwell on relation matrices to represent the structure of sample information. Based on this observation, we combine these methodologies by incorporating Gaussian kernel with fuzzy rough sets and propose a Gaussian kernel approximation based fuzzy rough set model. Fuzzy T-equivalence relations constitute the fundamentals of most fuzzy rough set models. It is proven that fuzzy relations with Gaussian kernel are reflexive, symmetric and transitive. Gaussian kernels are introduced to acquire fuzzy relations between samples described by fuzzy or numeric attributes in order to carry out fuzzy rough data analysis. Moreover, we discuss information entropy to evaluate the kernel matrix and calculate the uncertainty of the approximation. Several functions are constructed for evaluating the significance of features based on kernel approximation and fuzzy entropy. Algorithms for feature ranking and reduction based on the proposed functions are designed. Results of experimental analysis are included to quantify the effectiveness of the proposed methods. [Copyright &y& Elsevier]