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The k-nearest neighbour (KNN) is a simple and yet effective classification rule. To achieve robustness against outliers, several local mean-based extensions of the KNN classifier have been proposed which assign the query to the class with nearest local mean. However, using the conventional proximity measures causes poor performance in situations with multi-scale classes. Here, we propose a new local mean-based KNN classifier that uses a new modified distance measure which adjusts the proximity around each sample with respect to the situation and density. We scale the distance from the sample to each class with respect to the size of its influence set such that the sample seems closer to classes in which it has a higher number of reverse neighbours. We apply the proposed method on three local mean-based KNN classifiers. Our results show that the proposed method improves the performance of the local mean-based classifiers.

ABSTRACTTraffic speed is an important traffic parameter whose accurate prediction is crucial in traffic management. Different machine learning and ensemble methods have been used for predicting traffic parameters. Proper feature selection, the type of learners, ensemble methods, and appropriate aggregation technique have a significant impact on the prediction accuracy of ensemble methods. ‎To achieve higher prediction accuracy, using the nonlinear learning ability of Support Vector Regression (SVR) model, we propose a stacking ensemble learning model.‎‏‎ We train the base learners using different training subsets based on random subspace selection. In the analysis of empirical and complex data, Empirical Mode Decomposition (EMD) is used for decomposing time series data into seasonal and trend components. We use EMD to create new features. In order to reduce the computation time, we also propose a sample selection method. The results of our experiments show that the proposed method outperforms the existing hybrid EMD-ARIMA and non-ensemble models. In comparison with the baseline models, using the EMD technique leads to a 0.32 to 4% decrease in RMSE for different links. The results show that the proposed model outperforms the ARIMA and ARIMA-EMD models in terms of long-term traffic speed forecasting performance.

We herein propose a heuristic redundancy selection algorithm that combines resubmission, replication, and checkpointing redundancies to reduce the resiliency overhead in fault‐tolerant workflow scheduling. The appropriate combination of these redundancies for workflow tasks is obtained in two consecutive phases. First, to compute the replication vector (number of task replicas), we apportion the set of provisioned resources among concurrently executing tasks according to their needs. Subsequently, we obtain the optimal checkpointing interval for each task as a function of the number of replicas and characteristics of tasks and computational environment. We formulate the problem of obtaining the optimal checkpointing interval for replicated tasks in situations where checkpoint files can be exchanged among computational resources. The results of our simulation experiments, on both randomly generated workflow graphs and real‐world applications, demonstrated that both the proposed replication vector computation algorithm and the proposed checkpointing scheme reduced the resiliency overhead.

Byline: Vincent Curci, Hassan Motallebi and Kishan Kasondra, Los Angeles Department of Water and Power AS REGULATORY UNCERtainty is gripped over the past decade, it seems like we've majored in [...]