Prediction with Confidence Based on a Random Forest Classifier

Abstract : Conformal predictors represent a new flexible framework that outputs region predictions with a guaranteed error rate. Efficiency of such predictions depends on the nonconformity measure that underlies the predictor. In this work we designed new nonconformity measures based on a random forest classifier. Experiments demonstrate that proposed conformal predictors are more efficient than current benchmarks on noisy mass spectrometry data (and at least as efficient on other type of data) while maintaining the property of validity: they output fewer multiple predictions, and the ratio of mistakes does not exceed the preset level. When forced to produce singleton predictions, the designed conformal predictors are at least as accurate as the benchmarks and sometimes significantly outperform them.
Type de document :
Communication dans un congrès
Harris Papadopoulos; Andreas S. Andreou; Max Bramer. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. Springer, IFIP Advances in Information and Communication Technology, AICT-339, pp.37-44, 2010, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-16239-8_8〉
Liste complète des métadonnées

Littérature citée [10 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01060649
Contributeur : Hal Ifip <>
Soumis le : jeudi 16 novembre 2017 - 15:41:43
Dernière modification le : dimanche 17 décembre 2017 - 01:11:24

Fichier

DevetyarovN10.pdf
Fichiers produits par l'(les) auteur(s)

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Dmitry Devetyarov, Ilia Nouretdinov. Prediction with Confidence Based on a Random Forest Classifier. Harris Papadopoulos; Andreas S. Andreou; Max Bramer. 6th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations (AIAI), Oct 2010, Larnaca, Cyprus. Springer, IFIP Advances in Information and Communication Technology, AICT-339, pp.37-44, 2010, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-642-16239-8_8〉. 〈hal-01060649〉

Partager

Métriques

Consultations de la notice

67

Téléchargements de fichiers

2