Feature Ranking Based on Decision Border

Diamantini, Claudia; Gemelli, Alberto; Potena, Domenico

doi:10.1109/icpr.2010.154

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…Every FE algorithm can be applied as core of GOFR. In Diamantini et al (2010), the applicability of EDBFM as FE algorithm at the core of GOFR is demonstrated. A complete benchmarking of BVQFE, LDA, and OLDA algorithms is proposed in .…”

Section: Data Analysis Subsystemmentioning

confidence: 99%

GIS-Supported Decision Making

Gemelli¹,

Mancini²,

Diamantini

et al. 2013

GIS to Support Cost-Effective Decisions on Renewable Sources

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For the regional scale planning of the renewable energy production, it is required to systematically assess the availability of the resource and the convenience of its exploitation in each site individually. The GIS is the ideal tool to process large amounts of data that subtend the decision problem in a geographic dimension, to synthesize the models, and make them accessible. In this chapter, we present some analytical tools that are included in the architecture of GIS and extend its capabilities as a decision support system. The emphasis is on methods that automate issues such as selection of information, site classification, problems solving. An information system is outlined that has the ability to self-configure to maximize efficacy and efficiency of the data processing.

show abstract

Section: Data Analysis Subsystemmentioning

confidence: 99%

GIS-Supported Decision Making

Gemelli¹,

Mancini²,

Diamantini

et al. 2013

GIS to Support Cost-Effective Decisions on Renewable Sources

Self Cite

View full text Add to dashboard Cite

show abstract

“…Many researchers have developed different techniques for feature selection in data mining such as feature ranking based decision border [18], privacy preserving feature selection in P2P network [19], construction of fuzzy knowledge bases incorporating feature selection [20]. Other additional fuzzy methodologies have also been considered to enhance fuzzy model for feature selection such as higher order models for fuzzy random variables [21], upper and lower probabilities induced by fuzzy random variable [22], evolutionary boosting algorithms based on fuzzy rule classifiers [23], modeling vague data with fuzzy systems under a combination of crisp and imprecise criteria [24], fuzzy sets as basis for theory of possibility [25].…”

Section: Related Workmentioning

confidence: 99%