In the era of big data with exponential growth in volume, how to reduce security issues such as leakage caused by machine learning is a hot area recent research. The existing privacy budget allocation strategies are usually only suitable for applications specific spaces and cannot meet users' personalized needs allocation. Therefore, linear strategy proposed. assigns each layer linearly increas...

To improve the computational efficiency and classification accuracy in context of big data, an optimized parallel random forest algorithm is proposed based on Spark computing framework. First, a new Gini coefficient defined to reduce impact feature redundancy for higher accuracy. Next, number candidate split points calculations continuous features, approximate equal-frequency binning method det...

We extend the Chow-Liu algorithm for general random variables while the previous versions only considered finite cases. In particular, this paper applies the generalization to Suzuki’s learning algorithm that generates from data forests rather than trees based on the minimum description length by balancing the fitness of the data to the forest and the simplicity of the forest. As a result, we s...

Short-term power load forecasting provides important guidance for the improvement of marketing and control levels enterprises. In this paper, a novel method, named RF-TStacking, is proposed to forecast short-term load. This study starts from influence factors load, random forest applied estimate importance Based on Stacking strategy, integration LightGBM realized achieve forecasting. To improve...

Stock trend forecasting is a one of the main factors in choosing the best investment, hence prediction and comparison of different firms’ stock trend is one method for improving investment process. Stockholders need information for forecasting firm’s stock trend in order to make decision about firms’ stock trading. In this study stock trend, forecasting performs by data mining algorithm. It sho...

We propose a new parallelized high-dimensional single-query path planning technique that uses a coupled forest of random trees (i.e., instead of a single tree). We present both theoretical and experimental results that show using forests of random trees can lead to expected super linear speedup, with respect to the number of trees in the forest. In other words, with T trees running in parallel,...