Abstract Nowadays, people’s buying or availing services decisions are subject to online available reviews/opinions. The authenticity of these reviews/opinions is dubious, as there exist many fake reviews posted to attain monetary benefits by promoting their own or demoting the competitor’s products or services known as review spam. Although the number of spam is relatively less than that of normal reviews in real-life, this class imbalance is a critical concern in review spam detection. The performance degrades when the classifier skew towards the majority class. Moreover, efficient feature selection is essentially needed for this issue. The purpose of this study is to develop a framework based on different effective feature selection along with data balancing techniques. Validation results show that our proposed framework commendably copes up with the review spam issue and a higher precision on the real-life dataset. Further, we tested the sensitivity of our proposed framework using both parametric and non-parametric tests and found it significant.

Topic models have been prevailing for many years on discovering latent semantics while modeling long documents.However, for short texts they generally suffer from data sparsity because of extremely limited word cooccurrences; thus tend to yield repetitive or trivial topics with low quality.In this paper, to address this issue, we propose a novel neural topic model in the framework of autoencoding with a new topic distribution quantization approach generating peakier distributions that are more appropriate for modeling short texts.Besides the encoding, to tackle this issue in terms of decoding, we further propose a novel negative sampling decoder learning from negative samples to avoid yielding repetitive topics.We observe that our model can highly improve short text topic modeling performance.Through extensive experiments on real-world datasets, we demonstrate our model can outperform both strong traditional and neural baselines under extreme data sparsity scenes, producing high-quality topics.

Outsourcing data mining tasks is beneficial for data owners who either lack expertise in data mining or sufficient computing resources. However, directly releasing the original data would leak private information. Research on Privacy-Preserving Data Mining (PPDM) is dedicated to addressing this issue, the aim of this research is to reduce the risk of privacy violations and preserve the knowledge in the original data. However, most existing methods in the literature ignore the case in which service providers want to verify the integrity and authenticity of their clients’ data to avoid data tampering before performing data mining tasks. In this paper, a new method is proposed to extend the turtle shell algorithm of data hiding to protect the privacy of the original data and to acquire authentication functions simultaneously. The act of data perturbation is performed by replacing data values with their closest neighbors according to a reference matrix. Further, a message authentication code is hidden in the perturbed data to verify the integrity and authenticity of the perturbed data. The experimental results showed that the proposed method achieved the purpose of data perturbation and outperformed similar methods in satisfying the PPDM requirement.

In empirical data modelling, a model of system is built up from a set of cases that the system has observed. Eventually, the performance of the inducted model is dominated by the quality and quantity of observations. Feature transformation methods are widely used to improve quality of knowledge ext racted from observations to build up more accurate and robust model. In the paper, a new feature transformation method named dynamical feature bundling for decision tree algorithm is proposed. Dynamical feature bundling groups a set of features in the tree induction phase and it enables decision tree algorithms to 1) make use of features in one bundle together to make collective judgments in splitting phase; 2) learn more reliable and stable knowledge from feature bundles created based on domain knowledge of experts; 3) embed feature transformation step into tree induction phase, and therefore the extra pre-process step which are necessary for static feature transformation methods is inessential. Our experiments show 2%-9% improvements of AUC value on a very imbalanced dataset. Slight improvements are also obtained on a more balanced data set.

A company's local data is often insufficient for analyzing market trends and making reasonable business plans. Decision making must also be based on information from suppliers, partners and competitors. Systematically integrating suitable external data from the Web into a data warehouse is a meaningful solution and will benefit the enterprise. However, the autonomy and dynamics of the Web make the task of selecting relevant and qualified external data from the Web challenging. We develop a set of criteria for evaluating and selecting Web resources as external data sources of a data warehouse and discuss how to screen Web data sources using multi-criteria decision making (MCDM) methods. The final decision with respect to selecting Web sources is sensitive to critical factors, i.e., the criterion weight and performance score of alternatives in terms of each criterion. We analyzed the sensitivity of the final rank of alternatives in terms of critical factors in order to gain an insight into the stability of our final decision. The comparison of several MCDM approaches for Web source screening is also presented.