基于自适应增量集成学习的非平稳金融时间序列预测

金融市场对于社会经济的发展非常重要,因此金融时间序列预测(Financial time series prediction,FTSP)一直是人们研究的焦点。至今,许多基于统计分析和软计算的方法被提出以解决FTSP问题,其中大多数方法将金融时间序列(Financial time series,FTS)视为或转化为平稳序列进行处理。但是,由于绝大部分FTS是非平稳的,因此这些方法通常存在伪回归或预测性能不佳等问题。本文提出了一种自适应增量集成学习(Self-adaptive incremental ensemble learning,SIEL)算法,用于解决非平稳金融时间序列预测(Non-stationary FTSP,NS-FTSP)问题。SIEL算法的主要思想是为每个非平稳金融时间序列(Non-stationary FTS,NS-FTS)子集增量地训练一个基模型,然后使用自适应加权规则将各基模型组合起来。SIEL算法的重点在于数据权重和基模型权重的更新:数据权重基于当前集成模型在最新数据集上的性能进行更新,其目的不是为了数据采样,而是为了权衡误差;基模型权重基于其所处环境进行自适应更新,且基模型在越新环境下的性能应具有越高的权重。此外,针对NS-FTS的特征,SIEL算法提出了一种能协调新旧知识以及应对环境重演的策略。最后,给出了SIEL算法在3个NS-FTS数据集上的实验结果,并将其与已有算法进行了对比。实验结果表明,SIEL算法能很好地解决NS-FTSP问题。

基于5G通信的配电网馈线自动化切换系统设计

文章提出一种基于5G通信的配电网馈线自动化切换系统设计方案。该方案充分利用5G通信的低时延和高可靠性,保证了切换指令的高效传输。在馈线切换决策方面,结合XGBoost和LightGBM模型的优点,设计了一种自适应集成学习策略,在不同场景下动态调整两种模型的权重,显著提高了切换精度和负荷恢复率。

Combining supervised classifiers with unlabeled data

Ensemble learning is a wildly concerned issue.Traditional ensemble techniques are always adopted to seek better results with labeled data and base classifiers.They fail to address the ensemble task where only unlabeled data are available.A label propagation based ensemble(LPBE) approach is proposed to further combine base classification results with unlabeled data.First,a graph is constructed by taking unlabeled data as vertexes,and the weights in the graph are calculated by correntropy function.Average prediction results are gained from base classifiers,and then propagated under a regularization framework and adaptively enhanced over the graph.The proposed approach is further enriched when small labeled data are available.The proposed algorithms are evaluated on several UCI benchmark data sets.Results of simulations show that the proposed algorithms achieve satisfactory performance compared with existing ensemble methods.

Interactive image segmentation with a regression based ensemble learning paradigm

To achieve fine segmentation of complex natural images, people often resort to an interactive segmentation paradigm, since fully automatic methods often fail to obtain a result consistent with the ground truth. However, when the foreground and background share some similar areas in color, the fine segmentation result of conventional interactive methods usually relies on the increase o f manual labels. This paper presents a novel interactive image segmentation method via a regression-based ensemble model with semi-supervised learning. The task is formulated as a non-linear problem integrating two complementary spline regressors and strengthening the robustness of each regressor via semi-supervised learning. First, two spline regressors with a complementary nature are constructed based on multivariate adaptive regression splines （MARS） and smooth thin plate spline regression （TPSR）. Then, a regressor boosting method based on a clustering hypothesis and semi-supervised learning is proposed to assist the training of MARS and TPSR by using the region segmentation information contained in unlabeled pixels. Next, a support vector regression （SVR） based decision fusion model is adopted to integrate the results of MARS and TPSR. Finally, the GraphCut is introduced and combined with the SVR ensemble results to achieve image segmentation. Extensive experimental results on benchmark datasets of BSDS500 and Pascal VOC have demonstrated the effectiveness of our method, and the com- parison with experiment results has validated that the proposed method is comparable with the state-of-the-art methods for in- teractive natural image segmentation.