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A novel framework using a Bayesian approach for content-based phishing web page detection is presented. Our model takes into account textual and visual contents to measure the similarity between the protected web page and suspicious web pages. A text classifier, an image classifier, and an algorithm fusing the results from classifiers are introduced. An outstanding feature of this paper is the exploration of a Bayesian model to estimate the matching threshold. This is required in the classifier for determining the class of the web page and identifying whether the web page is phishing or not. In the text classifier, the naive Bayes rule is used to calculate the probability that a web page is phishing. In the image classifier, the earth mover's distance is employed to measure the visual similarity, and our Bayesian model is designed to determine the threshold. In the data fusion algorithm, the Bayes theory is used to synthesize the classification results from textual and visual content. The effectiveness of our proposed approach was examined in a large-scale dataset collected from real phishing cases. Experimental results demonstrated that the text classifier and the image classifier we designed deliver promising results, the fusion algorithm outperforms either of the individual classifiers, and our model can be adapted to different phishing cases. [ABSTRACT FROM AUTHOR]

The shortage of training samples remains one of the main obstacles in applying the neural networks to the hyperspectral images classification. To fuse the spatial and spectral information, pixel patches are often utilized to train a model, which may further aggregate this problem. In the existing works, an artificial neural network (ANN) model supervised by centerloss (ANNC) was introduced. Training merely with spectral information, the ANNC yields discriminative spectral features suitable for the subsequent classification tasks. In this paper, we propose a novel convolutional neural network (CNN)-based spatial feature fusion (CSFF) algorithm, which allows a smart integration of spatial information to the spectral features extracted by ANNC. As a critical part of CSFF, a CNN-based discriminant model is introduced to estimate whether two pixels belong to the same class. At the testing stage, by applying the discriminant model to the pixel pairs generated by a test pixel and each of its neighbors, the local structure is estimated and represented as a customized convolutional kernel. The spectral–spatial feature is generated by a convolutional operation between the estimated kernel and the corresponding spectral features within a local region. The final label is determined by classifying the resulting spectral–spatial feature. Without increasing the number of training samples or involving pixel patches at the training stage, the CSFF framework achieves the state of the art by declining 20%–50% classification failures in experiments on three well-known hyperspectral images. [ABSTRACT FROM AUTHOR]

This paper proposes an optimal feature selection approach, namely, probabilistic neural network-based feature selection (PFS), for power-quality disturbances classification. The PFS combines a global optimization algorithm with an adaptive probabilistic neural network (APNN) to gradually remove redundant and irrelevant features in noisy environments. To validate the practicability of the features selected by the proposed PFS approach, we employed three common classifiers: multilayer perceptron, k-nearest neighbor and APNN. The results indicate that this PFS approach is capable of efficiently eliminating nonessential features to improve the performance of classifiers, even in environments with noise interference. [ABSTRACT FROM AUTHOR]

We propose in this letter two new subspace learning methods, called nearest feature space analysis (NFSA) and discriminant nearest feature space analysis (DNFSA), for pattern classification. While many subspace learning algorithms have been proposed in recent years, most of them apply the conventional nearest neighbor (NN) metric to derive the subspace and may not effectively characterize the geometrical information of the samples, especially when the number of training samples per class is limited. In this paper, we propose using the nearest feature space (NFS) metric to seek a NFSA subspace to improve the discriminating power of the subspace for classification. To further enhance the discriminative power of NFSA, we also propose a new DNFSA method to minimize the within-class feature space (FS) distances and maximize the between-class FS distances simultaneously in the derived subspace. Experimental results on face and facial expression recognition are presented to demonstrate the efficacy of the proposed methods. [ABSTRACT FROM PUBLISHER]

A maritime automatic target recognition system is developed that performs ship classification using one-dimensional high resolution range profiles. Five physically based features are defined and are extracted from both VV and HH polarizations resulting in a 10-dimensional feature vector. A nonlinear classifier combination approach involving a neural network combiner along with three individual classifiers (Bayes, nearest neighbor, and neural network) is proposed. A decision confidence measure based on the classifier discriminants is developed using a nonparametric estimation approach. The confidence measure enables the system to reject samples that have a low decision confidence. The performance of the proposed neural network based combination is compared with individual classifiers and a number of other combination rules. The results show that this combination can provide high recognition accuracy along with a high probability of declaration. The performance in the presence of samples from not-before-seen classes is also investigated. A new nearest neighbor confidence thresholding approach is developed to aid the neural network combiner in rejecting these samples. [ABSTRACT FROM AUTHOR]