The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Feature Selection Algorithms for Classification and Clustering
Abstract
Feature selection is an important topic in data mining, especially for high dimensional dataset. Feature selection is a process commonly used in machine learning, wherein subsets of the features available from the data are selected for application of learning algorithm. The best subset contains the least number of dimensions that most contribute to accuracy. Feature selection methods can be decomposed into three main classes, one is filter method, another one is wrapper method and third one is embedded method. This chapter presents an empirical comparison of feature selection methods and its algorithm. In view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enable to adequately decide which algorithm to use in certain situation. This chapter reviews several fundamental algorithms found in the literature and assess their performance in a controlled scenario.
Related Content
Jaime Salvador, Zoila Ruiz, Jose Garcia-Rodriguez.
© 2020.
12 pages.
|
Stavros Pitoglou.
© 2020.
11 pages.
|
Mette L. Baran.
© 2020.
13 pages.
|
Yingxu Wang, Victor Raskin, Julia M. Rayz, George Baciu, Aladdin Ayesh, Fumio Mizoguchi, Shusaku Tsumoto, Dilip Patel, Newton Howard.
© 2020.
15 pages.
|
Yingxu Wang, Lotfi A. Zadeh, Bernard Widrow, Newton Howard, Françoise Beaufays, George Baciu, D. Frank Hsu, Guiming Luo, Fumio Mizoguchi, Shushma Patel, Victor Raskin, Shusaku Tsumoto, Wei Wei, Du Zhang.
© 2020.
18 pages.
|
Nayem Rahman.
© 2020.
24 pages.
|
Amir Manzoor.
© 2020.
27 pages.
|
|
|