Description: FASBIR(Filtered Attribute Subspace based bagging with Injected Randomness) is a variant of bagging algorithm, whose purpose is to improve accuracy of local learners, such as kNN, through multi-model perturbing ensemble.
Reference: Z.-H. Zhou and Y. Yu. Ensembling local learners through multimodal perturbation. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.4, pp.725-735.
標(biāo)簽：
Description
Randomness
Attribute
Filtered
上傳時間：
2015-04-10
上傳用戶：ynzfm
machine learning, accuracy estimation, cross-validation,
bootstrap, ID3, decision trees, decision graphs, naive-bayes,
decision tables, majority, induction algorithms, classifiers,
categorizers, general logic diagrams, instance-based algorithms,
discretization, lazy learning, bagging, MineSet.
標(biāo)簽：
decision
cross-validation
estimation
bootstrap
上傳時間：
2015-07-26
上傳用戶：趙云興
