Cost-Sensitive Boosting

A novel framework is proposed for the design of cost-sensitive boosting algorithms. The framework is based on the identification of two necessary conditions for optimal cost-sensitive learning that 1) expected losses must be minimized by optimal cost-sensitive decision rules and 2) empirical loss mi...

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Veröffentlicht in:	IEEE transactions on pattern analysis and machine intelligence 2011-02, Vol.33 (2), p.294-309
Hauptverfasser:	Masnadi-Shirazi, H, Vasconcelos, N
Format:	Artikel
Sprache:	eng
Schlagworte:	AdaBoost Algorithm design and analysis Algorithms Applied sciences Artificial intelligence asymmetric boosting Boosting Boundaries Calibration Computer science control theory systems Computer vision Cost engineering Cost function cost-sensitive learning Data processing. List processing. Character string processing Derivation Exact sciences and technology Face detection Laboratories Learning Medical diagnosis Memory organisation. Data processing Object detection Operations research Optimization Pattern analysis Pattern recognition. Digital image processing. Computational geometry Proposals Software Standard data Studies
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