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特征选择,基本原理与应用

Feature selection, fundamentals and applications
课程网址: http://videolectures.net/mmdss07_guyon_fsf/  
主讲教师: Isabelle Guyon
开课单位: 克洛平公司
开课时间: 2007-12-03
课程语种: 英语
中文简介:
变量和特征选择已经成为许多应用领域的研究热点,其中有数万或数十万个变量的数据集可用。这些领域包括网络文档的文本处理、基因表达阵列分析和组合化学。变量选择的目标有三个方面:提高预测因子的预测性能,提供更快、更具成本效益的预测因子,以及更好地了解生成数据的底层过程。本演示将涵盖这些问题的广泛方面:更好地定义目标函数、特征构造、特征排序、多变量特征选择、高效搜索方法和特征有效性评估方法。大多数特征选择方法并不试图揭示特征和目标之间的因果关系,而是专注于做出最佳预测。我们将研究因果关系知识有利于特征选择的情况。这些好处可能包括:解释因果机制的相关性,区分实际特征和实验伪影,预测外部因素的行为后果,以及在非平稳环境中进行预测。
课程简介: Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. These areas include text processing of internet documents, gene expression array analysis, and combinatorial chemistry. The objective of variable selection is three-fold: improving the prediction performance of the predictors, providing faster and more cost-effective predictors, and providing a better understanding of the underlying process that generated the data. This presentation will cover a wide range of aspects of such problems: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods. Most feature selection methods do not attempt to uncover causal relationships between feature and target and focus instead on making best predictions.We will examine situations in which the knowledge of causal relationships benefits feature selection. Such benefits may include: explaining relevance in terms of causal mechanisms, distinguishing between actual features and experimental artifacts, predicting the consequences of actions performed by external agents, and making predictions in non-stationary environments.
关 键 词: 变量和特征选择; 目标函数; 有效评估方法; 因果关系
课程来源: 视频讲座网
最后编审: 2020-07-30:yumf
阅读次数: 53