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Online travel has developed dramatically during the past three years in China. This results in a large amount of unstructured data like tourism reviews from which it is hard to extract useful knowledge. In this paper, a DWWP system consisting of domain-specific new words detection (DW) and word propagation (WP) is presented. DW deals with the negligence of user-invented new words and converted sentiment words by means of AMI (Assembled Mutual Information). Inspired by social networks, the new method WP incorporates manually calibrated sentiment scores, semantic and statistical similarity information, which improves the quality of sentiment lexicon in comparison with existing data-driven methods. Experimental results show that DWWP improves seventeen percentage points compared with graph propagation and four percentage points compared with label propagation in terms of accuracy on Dataset I and Dataset II, respectively.

A robust and sparse multi-class approach for Multi-Class classification is proposed.The proposed method is based on Ramp loss K-Support Vector Classification-Regression.The CCCP procedure is used to solve a non-differentiable non-convex optimization problem.ADMM is adopted to make our model well-adapted for the large-scale setting.The results of Ramp-KSVCR show superior generalization power and low computational cost. Network intrusion detection problem is an ongoing challenging research area because of a huge number of traffic volumes, extremely imbalanced data sets, multi-class of attacks, constantly changing the nature of new attacks and the attackers methods. Since the traditional network protection methods fail to adequately protect the computer networks, the need for some sophisticated methodologies has been felt. In this paper, we develop a precise, sparse and robust methodology for multi-class intrusion detection problem based on the Ramp Loss K-Support Vector Classification-Regression, named Ramp-KSVCR. The main objectives of this research are to address the following issues; 1) Highly imbalanced and skewed attacks distribution; hence, we utilized the K-SVCR model as a core of our model; 2) Sensitivity of SVM and its extensions to the presence of noises and outliers in the training sets, to cope with this problem, Ramp loss function is implemented to our model; 3) and since the proposed Ramp-KSVCR model is a non-differentiable non-convex optimization problem, we took ConcaveConvex Procedure (CCCP) to solve this model. Furthermore, we introduced Alternating Direction Method of Multipliers (ADMM) procedure to make our model well-adapted to be applicable in the large-scale setting and to reduce the training time. The performance of the proposed method has been evaluated by some artificial data and also by conducting some experiments with the NSL-KDD data set and UNSW-NB15 as a recently published intrusion detection data set. Experimental results not only demonstrate the superiority of the proposed method over the traditional approaches tested against it in terms of generalization power and sparsity but also saving a considerable amount of computational time.

Learning a classifier from groups of unlabeled data, only knowing, for each group, the proportions of data with particular labels, is an important branch of classification tasks that are conceivable in many practical applications. In this paper, we proposed a novel solution for the problem of learning with label proportions (LLP) based on nonparallel support vector machines, termed as proportion-NPSVM, which can improve the classifiers to be a pair of nonparallel classification hyperplanes. The unique property of our method is that it only needs to solve a pair of smaller quadratic programming problems. Moreover, it can efficiently incorporate the known group label proportions with the latent unknown observation labels into one optimization model under a large-margin framework. Compared to the existing approaches, there are several advantages shown as follows: 1) it does not need to make restrictive assumptions on the training data; 2) nonparallel classifiers can be achieved without computing the large inverse matrices; 3) the optimization model can be effectively solved by using the alternative strategy with SMO technique or SOR method; 4) proportion-NPSVM has better generalization ability. Sufficient experimental results on both binary-classes and multi-classes data sets show the efficiency of our proposed method in classification accuracy, which prove the state-of-the-art method for LLP problems compared with competing algorithms.

The novel node coupling clustering methods for link prediction are proposed.A new node coupling degree metric is proposed.The node coupling information and clustering information are used.Experimental evaluation about the effectiveness of our methods is presented. Due to the potential important information in real world networks, link prediction has become an interesting focus of different branches of science. Nevertheless, in "big data" era, link prediction faces significant challenges, such as how to predict the massive data efficiently and accurately. In this paper, we propose two novel node-coupling clustering approaches and their extensions for link prediction, which combine the coupling degrees of the common neighbor nodes of a predicted node-pair with cluster geometries of nodes. We then present an experimental evaluation to compare the prediction accuracy and effectiveness between our approaches and the representative existing methods on two synthetic datasets and six real world datasets. The experimental results show our approaches outperform the existing methods.

In this paper, we propose a novel sparse and robust nonparallel hyperplane classifier, named Ramp loss Nonparallel Support Vector Machine (RNPSVM), for binary classification. By introducing the Ramp loss function and also proposing a new non-convex and non-differentiable loss function based on the e-insensitive loss function, RNPSVM can explicitly incorporate noise and outlier suppression in the training process, has less support vectors and the increased sparsity leads to its better scaling properties. The non-convexity of RNPSVM can be efficiently solved by the Concave-Convex Procedure and experimental results on benchmark datasets confirm the effectiveness of the proposed algorithm.

It has been shown that the structural information of data may contain useful prior domain knowledge for training a classifier. How to apply the structural information of data to build a good classifier is a new research focus recently. As we all know, the all existing structural large margin methods are the common in considering all structural information within classes into one model. In fact, these methods do not balance all structural information's relationships both infra-class and inter-class, which directly results in these prior information not being exploited sufficiently. In this paper, we design a new Structural Twin Support Vector Machine (called S-TWSVM). Unlike existing methods based on structural information, S-TWSVM uses two hyperplanes to decide the category of new data, of which each model only considers one class's structural information and closer to the class at the same time far away from the other class. This makes S-TWSVM fully exploit these prior knowledge to directly improve the algorithm's the capacity of generalization. All experiments show that our proposed method is rigidly superior to the state-of-the-art algorithms based on structural information of data in both computation time and classification accuracy.