极限学习机在非线性划分数据上的Boosting分类
Classification with Boosting of Extreme Learning Machine Over Arbitrarily Partitioned Data
摘要：
在大数据时代，基于机器学习的计算智能方法被广泛用于分析大规模数据集。从这些类型的数据集中提取有用的预测模型是一个具有挑战性的问题，因为它们的高度复杂性。分析可用于获取业务价值的大量流数据是另一个需要解决的复杂问题。随着数据可用性（\textit{i.e. Big Data}）水平的提高，对它们的自动分类已成为一项重要而复杂的任务。因此，我们探索了应用基于MapReduce的分布式AdaBoosting的极限学习机（ELM）来构建分类模型的预测包的能力。因此，（i）创建数据集集合;（ii）使用ELM算法来构建弱学习器（分类器函数）;以及（iii）从一组弱学习器构建强学习器。我们将此训练模型应用于基准知识发现和数据采掘数据集。
英文摘要：
Machine learning based computational intelligence methods are widely used to analyze large scale data sets in this age of big data. Extracting useful predictive modeling from these types of data sets is a challenging problem due to their high complexity. Analyzing large amount of streaming data that can be leveraged to derive business value is another complex problem to solve. With high levels of data availability (\textit{i.e. Big Data}) automatic classification of them has become an important and complex task. Hence, we explore the power of applying MapReduce based Distributed AdaBoosting of Extreme Learning Machine (ELM) to build a predictive bag of classification models. Accordingly, (i) data set ensembles are created; (ii) ELM algorithm is used to build weak learners (classifier functions); and (iii) builds a strong learner from a set of weak learners. We applied this training model to the benchmark knowledge discovery and data mining data sets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
