No abstract

Software defect predictive model can efficiently help improve software quality and lessen testing effort. A large number of predictive models are proposed in a software engineering literature, but this paper presents the proposed method in software defect prediction with the comparative results based on two classifiers, i.e., backpropagation neural network and radial basis functions with Gaussian kernels as classifiers. Comparative results on NASA dataset are demonstrated and analyzed on the basis of mean square error and percent of accuracy. Experimental results show that the neural network performs better prediction than the RBF in almost subsets of data from 5.76% to 6.75%.

<p>Color name is one of the important features for computer vision. Many existing methods proposed to classify colors into a small number of color names. In this paper, we propose an alternative method with the goal to improve the accuracy for assigning a color name to an object in the given image. We here use the least-squares probabilistic classifiers (LSPC) with the local scaling parameters for solving this task. The benefit of the LSPC is that its solution can be computed analytically so that the obtained solution is global optimum, while the local scaling parameters play an important role to deal with the data including clusters with different local statistics as appeared in the real-world data. To deal with this task, the LSPC is learned to assign a color name to each pixel with the highest of the class-posterior density distribution. Then, the estimations of the class-posterior density distributions are utilized to compute the scores for predicting a color name to the given object. Lastly, the color name with the highest score is chosen as a predicted color name for that object. The experimental results on the eBay data set show the improvements over previously proposed<br />methods.</p>

We propose the feature selection method based on the dependency between features in an unsupervised manner. The underlying assumption is that the most important feature should provide high dependency between itself and the rest of the features. Therefore, the top m features with maximum dependency scores should be selected, but the redundant features should be ignored. To deal with this problem, the objective function that is applied to evaluate the dependency between features plays a crucial role. However, previous methods mainly used the mutual information (MI), where the MI estimator based on the k-nearest neighbor graph, resulting in its estimation dependent on the selection of parameter, k, without a systematic way to select it. This implies that the MI estimator tends to be less reliable. Here, we introduce the leastsquares quadratic mutual information (LSQMI) that is more sensible because its tuning parameters can be selected by cross-validation. We show through the experiments that the use of LSQMI performed better than that of MI. In addition, we compared the proposed method to the three counterpart methods using six UCI benchmark datasets. The results demonstrated that the proposed method is useful for selecting the informative features as well as discarding the redundant ones.

No abstract