Abstract
As computer and database technologies rapidly advance, human beings rely more and more on computers to accumulate data, process data, and make use of data. Machine learning, knowledge discovery, and data mining are some intelligent tools that help mankind accomplish those tasks. Researchers and practitioners realize that in order to use these tools effectively, an important part is pre-processing in which data is processed before it is presented to any learning, discovering, or visualizing algorithm. In many discovery applications (for example, marketing data analysis), a key operation is to find subsets of the population that behave enough alike to be worthy of focused analysis (Brackman and Anand, 1996). Although many learning methods attempt to select, extract, or construct features, both theoretical analyses and experimental studies indicate that many algorithms scale poorly in domains with large numbers of irrelevant and/or redundant features (Langley, 1996). All the evidence suggests the need for additional methods to overcome the difficulties.
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Liu, H., Motoda, H. (1998). Less Is More. In: Liu, H., Motoda, H. (eds) Feature Extraction, Construction and Selection. The Springer International Series in Engineering and Computer Science, vol 453. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5725-8_1
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DOI: https://doi.org/10.1007/978-1-4615-5725-8_1
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