Feature Selection for Knowledge Discovery and Data Mining:
As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The com puter generated data should be analyzed by computers; without the aid of computing technologies, it is certain that huge amounts of data collecte...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
1998
|
| Schriftenreihe: | The Springer International Series in Engineering and Computer Science
454 |
| Schlagworte: | |
| Online-Zugang: | BTU01 Volltext |
| Zusammenfassung: | As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The com puter generated data should be analyzed by computers; without the aid of computing technologies, it is certain that huge amounts of data collected will not ever be examined, let alone be used to our advantages. Even with today's advanced computer technologies (e. g. , machine learning and data mining sys tems), discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Taking its simplest form, raw data are represented in feature-values. The size of a dataset can be measUJ·ed in two dimensions, number of features (N) and number of instances (P). Both Nand P can be enormously large. This enormity may cause serious problems to many data mining systems. Feature selection is one of the long existing methods that deal with these problems. Its objective is to select a minimal subset of features according to some reasonable criteria so that the original task can be achieved equally well, if not better. By choosing a minimal subset offeatures, irrelevant and redundant features are removed according to the criterion. When N is reduced, the data space shrinks and in a sense, the data set is now a better representative of the whole data population. If necessary, the reduction of N can also give rise to the reduction of P by eliminating duplicates |
| Beschreibung: | 1 Online-Ressource (XXIII, 214 p) |
| ISBN: | 9781461556893 |
| DOI: | 10.1007/978-1-4615-5689-3 |
Internformat
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| 520 | |a As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The com puter generated data should be analyzed by computers; without the aid of computing technologies, it is certain that huge amounts of data collected will not ever be examined, let alone be used to our advantages. Even with today's advanced computer technologies (e. g. , machine learning and data mining sys tems), discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Taking its simplest form, raw data are represented in feature-values. The size of a dataset can be measUJ·ed in two dimensions, number of features (N) and number of instances (P). Both Nand P can be enormously large. This enormity may cause serious problems to many data mining systems. Feature selection is one of the long existing methods that deal with these problems. Its objective is to select a minimal subset of features according to some reasonable criteria so that the original task can be achieved equally well, if not better. By choosing a minimal subset offeatures, irrelevant and redundant features are removed according to the criterion. When N is reduced, the data space shrinks and in a sense, the data set is now a better representative of the whole data population. If necessary, the reduction of N can also give rise to the reduction of P by eliminating duplicates | ||
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Datensatz im Suchindex
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| author | Liu, Huan Motoda, Hiroshi |
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| dewey-full | 005.74 |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| doi_str_mv | 10.1007/978-1-4615-5689-3 |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:58Z |
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| isbn | 9781461556893 |
| language | English |
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| spelling | Liu, Huan Verfasser aut Feature Selection for Knowledge Discovery and Data Mining by Huan Liu, Hiroshi Motoda Boston, MA Springer US 1998 1 Online-Ressource (XXIII, 214 p) txt rdacontent c rdamedia cr rdacarrier The Springer International Series in Engineering and Computer Science 454 As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used. The com puter generated data should be analyzed by computers; without the aid of computing technologies, it is certain that huge amounts of data collected will not ever be examined, let alone be used to our advantages. Even with today's advanced computer technologies (e. g. , machine learning and data mining sys tems), discovering knowledge from data can still be fiendishly hard due to the characteristics of the computer generated data. Taking its simplest form, raw data are represented in feature-values. The size of a dataset can be measUJ·ed in two dimensions, number of features (N) and number of instances (P). Both Nand P can be enormously large. This enormity may cause serious problems to many data mining systems. Feature selection is one of the long existing methods that deal with these problems. Its objective is to select a minimal subset of features according to some reasonable criteria so that the original task can be achieved equally well, if not better. By choosing a minimal subset offeatures, irrelevant and redundant features are removed according to the criterion. When N is reduced, the data space shrinks and in a sense, the data set is now a better representative of the whole data population. If necessary, the reduction of N can also give rise to the reduction of P by eliminating duplicates Computer Science Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Computer science Data structures (Computer science) Artificial intelligence Merkmalsextraktion (DE-588)4314440-8 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Wissenstechnik (DE-588)4192641-9 gnd rswk-swf Wissenstechnik (DE-588)4192641-9 s Data Mining (DE-588)4428654-5 s Merkmalsextraktion (DE-588)4314440-8 s 1\p DE-604 Motoda, Hiroshi aut Erscheint auch als Druck-Ausgabe 9781461376040 https://doi.org/10.1007/978-1-4615-5689-3 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Liu, Huan Motoda, Hiroshi Feature Selection for Knowledge Discovery and Data Mining Computer Science Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Computer science Data structures (Computer science) Artificial intelligence Merkmalsextraktion (DE-588)4314440-8 gnd Data Mining (DE-588)4428654-5 gnd Wissenstechnik (DE-588)4192641-9 gnd |
| subject_GND | (DE-588)4314440-8 (DE-588)4428654-5 (DE-588)4192641-9 |
| title | Feature Selection for Knowledge Discovery and Data Mining |
| title_auth | Feature Selection for Knowledge Discovery and Data Mining |
| title_exact_search | Feature Selection for Knowledge Discovery and Data Mining |
| title_full | Feature Selection for Knowledge Discovery and Data Mining by Huan Liu, Hiroshi Motoda |
| title_fullStr | Feature Selection for Knowledge Discovery and Data Mining by Huan Liu, Hiroshi Motoda |
| title_full_unstemmed | Feature Selection for Knowledge Discovery and Data Mining by Huan Liu, Hiroshi Motoda |
| title_short | Feature Selection for Knowledge Discovery and Data Mining |
| title_sort | feature selection for knowledge discovery and data mining |
| topic | Computer Science Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Computer science Data structures (Computer science) Artificial intelligence Merkmalsextraktion (DE-588)4314440-8 gnd Data Mining (DE-588)4428654-5 gnd Wissenstechnik (DE-588)4192641-9 gnd |
| topic_facet | Computer Science Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Computer science Data structures (Computer science) Artificial intelligence Merkmalsextraktion Data Mining Wissenstechnik |
| url | https://doi.org/10.1007/978-1-4615-5689-3 |
| work_keys_str_mv | AT liuhuan featureselectionforknowledgediscoveryanddatamining AT motodahiroshi featureselectionforknowledgediscoveryanddatamining |