Feature Selection for Knowledge Discovery and Data Mining

Huan, Liu; Motoda, Hiroshi

doi:10.1007/978-1-4615-5689-3

Cited by 1,454 publications

(1,053 citation statements)

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“…We have taken our original pattern extraction method [11] for the case study described in Section IV. Attribute selection procedures are done by selecting relevant attributes manually or using attribute selection algorithms [7].…”

Section: A Procedures To Mine Time-series Rulesmentioning

confidence: 99%

Implementing an Integrated Time-Series Data Mining Environment Based on Temporal Pattern Extraction Methods: A Case Study of an Interferon Therapy Risk Mining for Chronic Hepatitis

Abe

Ohsaki

Yokoi

et al. 2006

New Frontiers in Artificial Intelligence

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-In this paper, we present the implementation of an integrated time-series data mining environment. Time-series data mining is one of key issues to get useful knowledge from data bases. To execute time-series data mining smoothly, we have designed an environment which integrates time-series pattern extraction methods, rule induction methods and rule evaluation methods with active human-system interaction. After implementing this environment, we have done a case study to mine time-series rules from blood/urine biochemical test data base on chronic hepatitis patients. Then a physician has evaluated and refined his hypothesis on this environment. We discuss the availability of how much support to mine interesting knowledge for an expert.

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Section: A Procedures To Mine Time-series Rulesmentioning

confidence: 99%

Implementing an Integrated Time-Series Data Mining Environment Based on Temporal Pattern Extraction Methods: A Case Study of an Interferon Therapy Risk Mining for Chronic Hepatitis

Abe

Ohsaki

Yokoi

et al. 2006

New Frontiers in Artificial Intelligence

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“…As in any other prediction method, classification trees have their predictive accuracy greatly affected by inconsistencies within the training dataset (LAGACHERIE & HOLMES, 1997). LIU & MOTODA (1998) contended that handling large datasets in a classification tree approach could be inefficient in terms of learning time and prediction accuracy, and proposed instance selection, a branch of statistical learning research, to handle datasets containing redundant and/or noisy instances as well as multi-collinearity. Instance selection is applied to reduce noise and redundant information in the whole dataset.…”

Section: Introductionmentioning

confidence: 99%

“…The challenge is to extract a representative subset that is small enough that can be handled easily by learning algorithms but still large enough that no relevant information is lost in the process. The main goal of instance selection is to reasonably reduce large datasets for faster predictions while preserving or even increasing accuracy (BUI et al, 1999;LIU & MOTODA, 1998). Advancing this research topic, SCHMIDT et al (2008) suggested that spatially constrained instance selection should be investigated in future pedometric research "focusing on the boundaries instead of concentrating on the more homogeneous core of the class areas".…”

Section: Introductionmentioning

confidence: 99%

Instance selection in digital soil mapping: a study case in Rio Grande do Sul, Brazil

et al. 2015

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“…This "curse of dimensionality", along with the expense of measuring additional features, motivates feature dimensionality reduction. Though no known deterministic algorithm finds the optimal feature set for a classification task, a wide range of feature selection algorithms may find near-optimal feature sets [2].…”

Section: Introductionmentioning

confidence: 99%

GA-Facilitated Knowledge Discovery and Pattern Recognition Optimization Applied to the Biochemistry of Protein Solvation

Peterson

Doom

Raymer

2004

Genetic and Evolutionary Computation – GECCO 2004

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Abstract. The authors present a GA optimization technique for cosinebased k-nearest neighbors classification that improves predictive accuracy in a class-balanced manner while simultaneously enabling knowledge discovery. The GA performs feature selection and extraction by searching for feature weights and offsets maximizing cosine classifier performance. GA-selected feature weights determine the relevance of each feature to the classification task. This hybrid GA/classifier provides insight to a notoriously difficult problem in molecular biology, the correct treatment of water molecules mediating ligand binding to proteins. In distinguishing patterns of water conservation and displacement, this method achieves higher accuracy than previous techniques. The data mining capabilities of the hybrid system improve the understanding of the physical and chemical determinants governing favored protein-water binding.

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Feature Selection for Knowledge Discovery and Data Mining

Cited by 1,454 publications

References 0 publications

Implementing an Integrated Time-Series Data Mining Environment Based on Temporal Pattern Extraction Methods: A Case Study of an Interferon Therapy Risk Mining for Chronic Hepatitis

Implementing an Integrated Time-Series Data Mining Environment Based on Temporal Pattern Extraction Methods: A Case Study of an Interferon Therapy Risk Mining for Chronic Hepatitis

Instance selection in digital soil mapping: a study case in Rio Grande do Sul, Brazil

GA-Facilitated Knowledge Discovery and Pattern Recognition Optimization Applied to the Biochemistry of Protein Solvation

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