LIU, H. and MOTODA, H. (1998): Feature Extraction, Construction, and Selection: A Data Mining Perspective. Kluwer. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Tailoring Representations to Different Requirements - Morik (Correct)
....the desired input output pair of languages. In this case, one of the options is to transform the data from the formalism in which they are available into the input format of the selected algorithm. The current interest in feature construction may stem from knowledge discovery in databases (KDD) [14]. The given database representation has to be transformed into one which is accepted by the learning algorithm. Of course, for an ILP learning algorithm there exists a 1:1 mapping from a database table to a predicate[7] However, this simple transformation most often is not one that eases ....
H. Liu and H. Motoda. Feature Extraction, Construction, and Selection: A Data Mining Perspective. Kluwer, 1998.
A Hybrid Approach to Feature Selection and.. - Ritthoff.. (2002) (4 citations) (Correct)
....features in a given feature set. Thus, feature selection is already implicitly done. Nevertheless one often needs an additional pre processing step prior to the application of the actual learning An overview of different approaches in feature abstraction, selection and construction is given in [17]. method. One reason is, that the prediction accuracy of many learning algorithms, including e.g. decision tree learners like C4.5 [21] decreases, when irrelevant or redundant features are added [13] Another problem particularly affecting the computation time is the lacking scalability of ....
H. Liu and H. Motoda. Feature Extraction, Construction, and Selection: A Data Mining Perspective. Kluwer, Dordrecht, NL, 1998.
The Representation Race - Preprocessing for Handling Time Phenomena - Morik (2000) (2 citations) (Correct)
....original representation. The central issue is to find appropriate restrictions and corresponding transformations for a given task [19] The problem of designing LE is not limited to the representation formalism but includes the selection or construction of appropriate features within a formalism [24]. The problem has become particularly urgent, since knowledge discovery confronts machine learning with databases that have been acquired and designed for processes different from learning. Given mature learning algorithms and the knowledge of their properties, the challenge is now to develop ....
H. Liu and H. Motoda. Feature Extraction, Construction, and Selection: A Data Mining Perspective. Kluwer, 1998.
Automatic Feature Extraction from Large - Time Series Ingo (Correct)
No context found.
LIU, H. and MOTODA, H. (1998): Feature Extraction, Construction, and Selection: A Data Mining Perspective. Kluwer.
Evolutionary Feature Space Transformation - Using Type-Restricted Generators (Correct)
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H. Liu and H. Motoda. Feature Extraction, Construction, and Selection: A Data Mining Perspective. Kluwer, Dordrecht, NL, 1998.
Features for Learning Local Patterns in Time-Stamped Data - Morik, Köpcke (2005) (Correct)
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Liu, H., Motoda, H.: Feature Extraction, Construction, and Selection: A Data Mining Perspective. Kluwer (1998)
A Hybrid Approach to Feature Selection and.. - , Klinkenberg.. (2002) (4 citations) (Correct)
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H. Liu and H. Motoda. Feature Extraction, Construction, and Selection: A Data Mining Perspective. Kluwer, Dordrecht, NL, 1998.
Automatic Feature Extraction from Large Time Series - Mierswa (2004) (Correct)
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LIU, H. and MOTODA, H. (1998): Feature Extraction, Construction, and Selection: A Data Mining Perspective. Kluwer.
Features for Learning Local Patterns in Time-Stamped Data - Morik, Köpcke (2005) (Correct)
No context found.
Liu, H., Motoda, H.: Feature Extraction, Construction, and Selection: A Data Mining Perspective. Kluwer (1998)
A Hybrid Approach to Feature Selection and.. - Ritthoff.. (2002) (4 citations) (Correct)
No context found.
H. Liu and H. Motoda. Feature Extraction, Construction, and Selection: A Data Mining Perspective. Kluwer, Dordrecht, NL, 1998.
Evolutionary Feature Space Transformation Using.. - Ritthoff, Klinkenberg (2003) (Correct)
No context found.
H. Liu and H. Motoda. Feature Extraction, Construction, and Selection: A Data Mining Perspective. Kluwer, Dordrecht, NL, 1998.
A Hybrid Approach to Feature Selection and.. - Ritthoff.. (2002) (4 citations) (Correct)
No context found.
H. Liu and H. Motoda. Feature Extraction, Construction, and Selection: A Data Mining Perspective. Kluwer, Dordrecht, NL, 1998.
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