1998 FeatureSelectionForKDD
- (Liu & Motoda, 1998) ⇒ Huan Liu, and Hiroshi Motoda. (1998). “Feature Selection for Knowledge Discovery and Data Mining.” Kluwer Academic.
Subject Headings: Feature Selection.
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Abstract
With advanced computer technologies and their omnipresent usage, data accumulates in a speed unmatchable by the human's capacity to process data. To meet this growing challenge, the research community of knowledge discovery from databases emerged. The key issue studied by this community is, in layman's terms, to make advantageous use of large stores of data. In order to make raw data useful, it is necessary to represent, process, and extract knowledge for various applications. Feature Selection for Knowledge Discovery and Data Mining offers an overview of the methods developed since the 1970's and provides a general framework in order to examine these methods and categorize them. This book employs simple examples to show the essence of representative feature selection methods and compares them using data sets with combinations of intrinsic properties according to the objective of feature selection. In addition, the book suggests guidelines for how to use different methods under various circumstances and points out new challenges in this exciting area of research. Feature Selection for Knowledge Discovery and Data Mining is intended to be used by researchers in machine learning, data mining, knowledge discovery, and databases as a toolbox of relevant tools that help in solving large real-world problems. This book is also intended to serve as a reference book or secondary text for courses on machine learning, data mining, and databases.
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5.6.1 Prior knowledge
The study of types of knowledge regarding features can help us in applying knowledge to feature selection. We discuss different types of knowledge related to feature selection and how knowledge can help in removing irrelevant and/or redundant features, and noise in the data.
- Relevance knowledge. It indicated that a group of features is relevant to a particular goal class. Strong relevant knowledge along can eliminate irrelevant and/or redundant features. Weak relevant knowledge can help guide the search for optimal features.
- Noise knowledge. This type of knowledge provides information about types of noise and level of noise in the data.
- Correlation knowledge. Knowing that some features are highly correlated to some others can help removing redundant features.
- The basic rule in applying knowledge to feature selection is in applying it first.
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 1998 FeatureSelectionForKDD | Huan Liu Hiroshi Motoda | Feature Selection for Knowledge Discovery and Data Mining | http://portal.acm.org/citation.cfm?id=551944 | 1998 |