Anomaly Detection for imbalanced datasets with Deep Generative Models

Many important data analysis applications present with severely imbalanced datasets with respect to the target variable. A typical example is medical image analysis, where positive samples are scarce, while performance is commonly estimated against the correct detection of these positive examples. We approach this challenge by formulating the problem as anomaly detection with generative models. We train a generative model without supervision on the `negative' (common) datapoints and use this model to estimate the likelihood of unseen data. A successful model allows us to detect the `positive' case as low likelihood datapoints. In this position paper, we present the use of state-of-the-art deep generative models (GAN and VAE) for the estimation of a likelihood of the data. Our results show that on the one hand both GANs and VAEs are able to separate the `positive' and `negative' samples in the MNIST case. On the other hand, for the NLST case, neither GANs nor VAEs were able to capture the complexity of the data and discriminate anomalies at the level that this task requires. These results show that even though there are a number of successes presented in the literature for using generative models in similar applications, there remain further challenges for broad successful implementation.Comment: 15 pages, 13 figures, accepted by Benelearn 2018 conferenc

Profiling student smokers:a behavioral approach

The aim of the present study is to construct a coherent profile of student smokers in Greece, based on their behavioral and demographic characteristics. In this context, we collected data by administrating an anonymous self-completed questionnaire, which was answered by students of University and Technological Educational Institute (T.E.I.) of Patras. The final sample consists of 1,190 student smokers. For the purposes of the present study, principal component analysis was utilized to explore and detect the demographic and behavioral profiles of Greek student smokers. The factor solution identified 5 demographic factors and 14 behavioral factors. All factors were labeled, interpreted and discussed in the light of existing knowledge in order to understand better the consumer behavior of student smokers

Profiling student smokers:a behavioral approach

The aim of the present study is to construct a coherent profile of student smokers in Greece, based on their behavioral and demographic characteristics. In this context, we collected data by administrating an anonymous self-completed questionnaire, which was answered by students of University and Technological Educational Institute (T.E.I.) of Patras. The final sample consists of 1,190 student smokers. For the purposes of the present study, principal component analysis was utilized to explore and detect the demographic and behavioral profiles of Greek student smokers. The factor solution identified 5 demographic factors and 14 behavioral factors. All factors were labeled, interpreted and discussed in the light of existing knowledge in order to understand better the consumer behavior of student smokers