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To improve the classification performance of support vector machines (SVMs) on imbalanced datasets, cost-sensitive learning methods have been proposed, e.g., Different Error Costs (DEC) and Fuzzy SVM for Class Imbalance Learning (FSVM-CIL). They relocate the hyperplane by adjusting the costs associated with misclassifying samples. However, the error costs are determined either empirically or by performing an exhaustive search in the parameter space. Both strategies can not guarantee effectiveness and efficiency simultaneously. In this paper, we propose ATEC, a solution that can efficiently find a preferable hyperplane by automatically tuning the error cost for between-class samples. ATEC distinguishes itself from all existing parameter tuning strategies by two main features: (1) it can evaluate how effective an error cost is in terms of classification accuracy; and (2) it changes the error cost in the right direction if it is not effective. Extensive experiments show that compared with the state-of-art methods, SVMs that are equipped with ATEC can not only obtain comparable improvements in terms of F1 score of minority class, area under the precision-recall curve (AUC-PR) and area under the ROC curve (AUC-ROC) scores, but also outperform the grid-search parameter tuning strategy by two orders of magnitude in terms of the training time when a high F1 score is required. [ABSTRACT FROM AUTHOR]

Nowadays, many location-based applications require the ability of querying $k$ -nearest neighbors over a very large scale of moving objects in road networks, e.g., taxi-calling and ride-sharing services. Traditional grid index with equal-sized cells can not adapt to the skewed distribution of moving objects in real scenarios. Thus, to obtain the fast querying response time, the grid needs to be split into more smaller cells which introduces the side-effect of higher memory cost, i.e., maintaining such a large volume of cells requires a much larger memory space at the server side. In this paper, we present $\sf {SIMkNN}$ , a scalable and in-memory k NN query processing technique. $\sf {SIMkNN}$ is dual-index driven, where we adopt a R-tree to store the topology of the road network and a hierarchical grid model to manage the moving objects in non-uniform distribution. To answer a kNN query in real time, $\sf {SIMkNN}$ adopts the strategy that incrementally enlarges the search area for network distance based nearest neighbor evaluation. It is far from trivial to perform the space expansion within the hierarchical grid index. For a given cell, we first define its neighbors in different directions, then propose a cell communication technique which allows each cell in the hierarchical grid index to be aware of its neighbors at any time. Accordingly, an efficient space expansion algorithm to generate the estimation area is proposed. The experimental evaluation shows that $\sf {SIMkNN}$ outperforms the baseline algorithm in terms of time and memory efficiency. [ABSTRACT FROM AUTHOR]