Massive Open Online Courses (MOOCs) have boomed in recent years because learners can arrange learning at their own pace. High dropout rate is a universal but unsolved problem in MOOCs. Dropout prediction has received much attention recently. A previous study reported the problem of learning behavior discrepancy leading to a wide range of fluctuation of prediction results. Besides, previous methods require iterative training which is time intensive. To address these problems, we propose DT-ELM, a novel hybrid algorithm combining decision tree and extreme learning machine (ELM), which requires no iterative training. The decision tree selects features with good classification ability. Further, it determines enhanced weights of the selected features to strengthen their classification ability. To achieve accurate prediction results, we optimize ELM structure by mapping the decision tree to ELM based on the entropy theory. Experimental results on the benchmark KDD 2015 dataset demonstrate the effectiveness of DT-ELM, which is 12.78%, 22.19%, and 6.87% higher than baseline algorithms in terms of accuracy, AUC, and F1-score, respectively.

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For a detection problem, a user often has some prior knowledge about the structure-specific subgraphs of interest, but few traditional approaches are capable of employing this knowledge. The main technical challenge is that few approaches can efficiently model the space of connected subgraphs that are isomorphic to a query graph. We present a novel, efficient approach for optimizing a generic nonlinear cost function subject to a query-specific structural constraint. Our approach enjoys strong theoretical guarantees on the convergence of a nearly optimal solution and a low time complexity. For the case study, we specialize the nonlinear function to several well-known graph scan statistics for anomalous subgraph discovery. Empirical evidence demonstrates that our method is superior to stateof-the-art methods in several real-world anomaly detection tasks.

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