2003
DOI: 10.1007/978-3-540-45231-7_7
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A Novel Partial-Memory Learning Algorithm Based on Grey Relational Structure

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Cited by 4 publications
(2 citation statements)
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“…In addition to deal with classification tasks, the above klevel grey relational structure can be used for instance pruning or partial memory learning [21,26,40]. For example, an instance may not be connected by any inward edges from Table 1 Average accuracy (%) of classification for the proposed approach and other methods with HOEM (with k-nn), HVDM (with k-nn) and IVDM (with k-nn) [39], respectively.…”
Section: An Instance-based Learning Algorithm Based On Grey Relationamentioning
confidence: 99%
“…In addition to deal with classification tasks, the above klevel grey relational structure can be used for instance pruning or partial memory learning [21,26,40]. For example, an instance may not be connected by any inward edges from Table 1 Average accuracy (%) of classification for the proposed approach and other methods with HOEM (with k-nn), HVDM (with k-nn) and IVDM (with k-nn) [39], respectively.…”
Section: An Instance-based Learning Algorithm Based On Grey Relationamentioning
confidence: 99%
“…The GIA method, which is based on number theory, can be easily used in decision problems that do not create certainty and where there is not enough information about alternatives (Chan and Tong, 2007). According to Huang and Lee (2003), the GIA also provides a clear and precise definition of all the relationships within a system of all existing situations in certain study subjects. It is known that the GIA method has advantages such as allowing many criteria to be handled together, being able to evaluate even when the number of data is small, lack of strict rules for the sample size, and allowing ranking according to the degree of relationship in cases where the distribution is unknown or not normal (Liu and Forrest, 2007).…”
Section: Giamentioning
confidence: 99%