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Title: Features handling by conformal predictors
Author: Yang, Meng
ISNI:       0000 0004 8498 0690
Awarding Body: Royal Holloway, University of London
Current Institution: Royal Holloway, University of London
Date of Award: 2015
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Unlike many conventional machine learning methods, conformal predictors allow to supply individual predictions with valid measurement of confidence. In this thesis we adapt conformal predictors to address three common problems related to feature handling. First of all, we consider the problem of feature selection in the context of conformal predictors. The main idea of our method is to use confidence measures as an indicator of usefulness of di↵erent feature subsets. The second one is the problem of how to utilize the additional information which is only available in training set. Recently, Vapnik proposed a novel learning paradigm to incorporate additional information within SVM algorithm. Inspired by Vapnik's method, we propose an approach to deal with additional information by conformal predictors. The last problem is classification using features with missing information. Conventionally, missing information is dealt with in pre-processing step, either by ignoring it or imputing it. We suggest a method which embeds the processing of missing information within conformal predictors. Experiments have been carried out to evaluate the proposed methods using public datasets. Results demonstrate the e↵ectiveness of these methods for feature handling.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available