Selecting the best classifier among the available ones is a difficult task, especially when only instances of one class exist. In this work we examine the notion of combining one-class classifiers as an alternative for selecting the best classifier. In particular, we propose two one-class classification performance measures to weigh classifiers and show that a simple ensemble that implements these measures can outperform the most popular one-class ensembles. Furthermore, we propose a new one-class ensemble scheme, TUPSO, which uses meta-learning to combine one-class classifiers. Our experiments demonstrate the superiority of TUPSO over all other tested ensembles and show that the TUPSO performance is statistically indistinguishable from that of the hypothetical best classifier.
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Temporal dynamic graphs are graphs whose topology evolves over time, with nodes and edges added and removed between different time snapshots. Embedding such graphs in a low-dimensional space is important for a variety of tasks, including graphs' similarities, time series trends analysis and anomaly detection, graph visualization, graph classification, and clustering. Despite the importance of the temporal element in these tasks, existing graph embedding methods focus on capturing the graph's nodes in a static mode and/or do not model the graph in its entirety in temporal dynamic mode. In this study, we present tdGraphEmbed, a novel temporal graph-level embedding approach that extend the random-walk based node embedding methods to globally embed both the nodes of the graph and its representation at each time step, thus creating representation of the entire graph at each step. Our approach was applied to graph similarity ranking, temporal anomaly detection, trend analysis, and graph visualizations tasks, where we leverage our temporal embedding in a fast and scalable way for each of the tasks. An evaluation of tdGraphEmbed on five real-world datasets shows that our approach can outperform state-of-the-art approaches used for graph embedding and node embedding in temporal graphs.
This paper introduces a new ensemble technique, cluster-based concurrent decomposition (CBCD) that induces an ensemble of classifiers by decomposing the training set into mutually exclusive sub-samples of equal-size. The CBCD algorithm first clusters the instance space by using the K-means clustering algorithm. Afterwards it produces disjoint sub-samples using the clusters in such a way that each sub-sample is comprised of tuples from all clusters and hence represents the entire dataset. An induction algorithm is applied in turn to each subset, followed by a voting mechanism that combines the classifier's predictions. The CBCD algorithm has two tuning parameters: the number of clusters and the number of subsets to create. Using a suitable meta-learning it is possible to tune these parameters properly. In the experimental study we conducted, the CBCD algorithm, using an embedded C4.5 algorithm, outperformed the bagging algorithm of the same computational complexity.
Context-aware recommender systems (CARSs) apply sensing and analysis of user context in order to provide personalized services. Adding context to a recommendation model is challenging, since the addition of context may increases both the dimensionality and sparsity of the model. Recent research has shown that modeling contextual information as a latent vector may address the sparsity and dimensionality challenges. We suggest a new latent modeling of sequential context by generating sequences of contextual information and reducing their contextual space to a compressed latent space.We train a long short-term memory (LSTM) encoder-decoder network on sequences of contextual information and extract sequential latent context from the hidden layer of the network in order to represent a compressed representation of sequential data. We propose new context-aware recommendation models that extend the neural collaborative filtering approach and learn nonlinear interactions between latent features of users, items, and contexts which take into account the sequential latent context representation as part of the recommendation process. We deployed our approach using two context-aware datasets with different context dimensions. Empirical analysis of our results validates that our proposed sequential latent context-aware model (SLCM), surpasses state of the art CARS models.
The high adoption of smart mobile devices among consumers provides an opportunity for e‐commerce retailers to increase their sales by recommending consumers with real time, personalized coupons that take into account the specific contextual situation of the consumer. Although context‐aware recommender systems (CARS) have been widely analyzed, personalized pricing or discount optimization in recommender systems to improve recommendations' accuracy and commercial KPIs has hardly been researched. This article studies how to model user‐item personalized discount sensitivity and incorporate it into a real time contextual recommender system in such a way that it can be integrated into a commercial service. We propose a novel approach for modeling context‐aware user‐item personalized discount sensitivity in a sparse data scenario and present a new CARS algorithm that combines coclustering and random forest classification (CBRF) to incorporate the personalized discount sensitivity. We conducted an experimental study with real consumers and mobile discount coupons to evaluate our solution. We compared the CBRF algorithm to the widely used context‐aware matrix factorization (CAMF) algorithm. The experimental results suggest that incorporating personalized discount sensitivity significantly improves the consumption prediction accuracy and that the suggested CBRF algorithm provides better prediction results for this use case.
Each new identified security threat class triggers new research and development efforts by the scientific and professional communities. In this study, we investigate the rate at which the scientific and professional communities react to new identified threat classes as it is reflected in the number of patents, scientific articles and professional publications over a long period of time. The following threat classes were studied: Phishing; SQL Injection; BotNet; Distributed Denial of Service; and Advanced Persistent Threat. Our findings suggest that in most cases it takes a year for the scientific community and more than two years for industry to react to a new threat class with patents. Since new products follow patents, it is reasonable to expect that there will be a window of approximately two to three years in which no effective product is available to cope with the new threat class.
Selecting the best classifier among the available ones is a difficult task, especially when only instances of one class exist. In this work we examine the notion of combining one-class classifiers as an alternative for selecting the best classifier. In particular, we propose two new one-class classification performance measures to weigh classifiers and show that a simple ensemble that implements these measures can outperform the most popular one-class ensembles. Furthermore, we propose a new one-class ensemble scheme, TUPSO, which uses meta-learning to combine one-class classifiers. Our experiments demonstrate the superiority of TUPSO over all other tested ensembles and show that the TUPSO performance is statistically indistinguishable from that of the hypothetical best classifier.
Recommender systems (RS) can now be found in many commercial Web sites, often presenting customers with a short list of additional products that they might purchase. Many commercial sites do not typically have the ability and resources to develop their own system and may outsource the RS to a third party. This had led to the growth of a recommendation as a service industry, where companies, referred to as RS providers, provide recommendation services. These companies must carefully balance the cost of building recommendation models and the payment received from the e-business, as these payments are expected to be low. In such a setting, restricting the computational time required for model building is critical for the RS provider to be profitable. In this article, we propose anytime algorithms as an attractive method for balancing computational time and the recommendation model performance, thus tackling the RS provider problem. In an anytime setting, an algorithm can be stopped after any amount of computational time, always ensuring that a valid, although suboptimal, solution will be returned. Given sufficient time, however, the algorithm should converge to an optimal solution. In this setting, it is important to evaluate the quality of the returned solution over time, monitoring quality improvement. This is significantly different from traditional evaluation methods, which mostly estimate the performance of the algorithm only after its convergence is given sufficient time. We show that the popular item-item top-N recommendation approach can be brought into the anytime framework by smartly considering the order by which item pairs are being evaluated. We experimentally show that the time-accuracy trade-off can be significantly improved for this specific problem.
