Outlier detection is an important topic in machine learning and has been used in a wide range of applications. In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution. However, due to the sparsity of data in high-dimensional space, a limited number of potential outliers may fail to provide sufficient information to assist the classifier in describing a boundary that can separate outliers from normal data effectively. To address this, we propose a novel Single-Objective Generative Adversarial Active Learning (SO-GAAL) method for outlier detection, which can directly generate informative potential outliers based on the mini-max game between a generator and a discriminator. Moreover, to prevent the generator from falling into the mode collapsing problem, the stop node of training should be determined when SO-GAAL is able to provide sufficient information. But without any prior information, it is extremely difficult for SO-GAAL. Therefore, we expand the network structure of SO-GAAL from a single generator to multiple generators with different objectives (MO-GAAL), which can generate a reasonable reference distribution for the whole dataset. We empirically compare the proposed approach with several state-of-the-art outlier detection methods on both synthetic and real-world datasets. The results show that MO-GAAL outperforms its competitors in the majority of cases, especially for datasets with various cluster types or high irrelevant variable ratio. The experiment codes are available at: https://github.com/leibinghe/GAAL-based-outlier-detection

Purpose The purpose of this paper is to investigate the influence of social commerce constructs (SCCs), social support and relationship quality on social commerce intentions, which lead to use behaviour of social networking sites for social commerce. Design/methodology/approach Data were collected from 343 users of social networking sites in Pakistan. The data analysis was conducted using PLS-SEM. Findings The results show that SCCs have empowered consumers through the existence of virtual groups, ratings and reviews and recommendations and referrals, thereby having a significant impact on social commerce intentions. The relationship quality with social networking sites, measured through commitment, satisfaction and trust, also proved to be a leading forecaster of social commerce intentions. The impact of social support could not positively influence the relationship quality with the social networking site. However, social support influences the social commerce intentions significantly. Research limitations/implications Future research should enrich model with some moderating variables and data may be collected from actual online shoppers only. Practical implications This study provides valuable insights to retailers to formulate their social commerce strategies as per decision factors results to have maximum engagement of consumers in social commerce. Originality/value The study proposes the unique model for finding the social commerce intentions and use behaviour using social support theory, relationship marketing theory and information systems literature.

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