Matches in SemOpenAlex for { <https://semopenalex.org/work/W1836715957> ?p ?o ?g. }
- W1836715957 abstract "Ensemble methods like Bagging and Boosting which combine the decisions of multiple hypotheses are some of the strongest existing machine learning methods. The diversity of the members of an ensemble is known to be an important factor in determining its generalization error. In this thesis, we present a new method for generating ensembles, DECORATE (Diverse Ensemble Creation by Oppositional Relabeling of Artificial Training Examples), that directly constructs diverse hypotheses using additional artificially-generated training examples. Query by Committee is one effective approach to active learning in which disagreement within the ensemble of hypotheses is used to select examples for labeling. Query by Bagging and Query by Boosting are two practical implementations of this approach that use Bagging and Boosting respectively, to build the committees. For efficient active learning it is critical that the committee be made up of consistent hypotheses that are very different from each other. Since DECORATE explicitly builds such committees, it is well-suited for this task. We introduce a new algorithm, ACTIVED ECORATE, which uses DECORATE committees to select good training examples. Experimental results demonstrate that ACTIVED ECORATE typically requires labeling fewer examples to achieve the same accuracy as Query by Bagging and Query by Boosting. Apart from optimizing classification accuracy, in many applications, producing good class probability estimates is also important, e.g., in fraud detection, which has unequal misclassification costs. This thesis introduces a novel approach to active learning based on ACTIVEDECORATE which uses Jensen-Shannon divergence (a similarity measure for probability distributions) to improve the selection of training examples for optimizing probability estimation. Comprehensive experimental results demonstrate the benefits of our approach. Unlike the active learning setting, in many learning problems the class labels for all instances are known, but feature values may be missing and can be acquired at a cost. For building accurate predictive models, acquiring complete information for all instances is often quite expensive, while acquiring information for a random subset of instances may not be optimal. We formalize the task of active feature-value acquisition, which tries to reduce the cost of achieving a desired model accuracy by identifying instances for which obtaining complete information is most informative. We present an approach, based on DECORATE, in which instances are selected for acquisition based on the current model's accuracy and its confidence in the prediction. Experimental results demonstrate that our approach can induce accurate models using substantially fewer feature-value acquisitions than random sampling." @default.
- W1836715957 created "2016-06-24" @default.
- W1836715957 creator A5008715111 @default.
- W1836715957 creator A5038700517 @default.
- W1836715957 date "2005-01-01" @default.
- W1836715957 modified "2023-09-28" @default.
- W1836715957 title "Creating diverse ensemble classifiers to reduce supervision" @default.
- W1836715957 cites W107306860 @default.
- W1836715957 cites W142683009 @default.
- W1836715957 cites W1480376833 @default.
- W1836715957 cites W1484084878 @default.
- W1836715957 cites W1495565561 @default.
- W1836715957 cites W1507090614 @default.
- W1836715957 cites W1509515766 @default.
- W1836715957 cites W1513874326 @default.
- W1836715957 cites W1514707997 @default.
- W1836715957 cites W1519196800 @default.
- W1836715957 cites W1525410796 @default.
- W1836715957 cites W1528361845 @default.
- W1836715957 cites W1529196404 @default.
- W1836715957 cites W1534477342 @default.
- W1836715957 cites W1534707631 @default.
- W1836715957 cites W1536929369 @default.
- W1836715957 cites W1546113605 @default.
- W1836715957 cites W1548189207 @default.
- W1836715957 cites W1552624648 @default.
- W1836715957 cites W1553262910 @default.
- W1836715957 cites W1562418542 @default.
- W1836715957 cites W1593345689 @default.
- W1836715957 cites W1594451427 @default.
- W1836715957 cites W1598033630 @default.
- W1836715957 cites W1604374636 @default.
- W1836715957 cites W1605688901 @default.
- W1836715957 cites W1633338417 @default.
- W1836715957 cites W1819386543 @default.
- W1836715957 cites W1850527962 @default.
- W1836715957 cites W1854064769 @default.
- W1836715957 cites W1966280301 @default.
- W1836715957 cites W1975846642 @default.
- W1836715957 cites W1979648751 @default.
- W1836715957 cites W1983479840 @default.
- W1836715957 cites W1991418450 @default.
- W1836715957 cites W1995945562 @default.
- W1836715957 cites W2015437745 @default.
- W1836715957 cites W2018770010 @default.
- W1836715957 cites W2019778169 @default.
- W1836715957 cites W2027902935 @default.
- W1836715957 cites W2044758663 @default.
- W1836715957 cites W2047253786 @default.
- W1836715957 cites W205184011 @default.
- W1836715957 cites W2053463056 @default.
- W1836715957 cites W2061119986 @default.
- W1836715957 cites W2080021732 @default.
- W1836715957 cites W2084812512 @default.
- W1836715957 cites W2098203240 @default.
- W1836715957 cites W2098737324 @default.
- W1836715957 cites W2098933520 @default.
- W1836715957 cites W2100805904 @default.
- W1836715957 cites W2106252139 @default.
- W1836715957 cites W2108949035 @default.
- W1836715957 cites W2108955812 @default.
- W1836715957 cites W2109001240 @default.
- W1836715957 cites W2112076978 @default.
- W1836715957 cites W2113882472 @default.
- W1836715957 cites W2115629999 @default.
- W1836715957 cites W2122772832 @default.
- W1836715957 cites W2123504579 @default.
- W1836715957 cites W2127816222 @default.
- W1836715957 cites W2128073546 @default.
- W1836715957 cites W2135293965 @default.
- W1836715957 cites W2137507956 @default.
- W1836715957 cites W2141518341 @default.
- W1836715957 cites W2144087279 @default.
- W1836715957 cites W2144578442 @default.
- W1836715957 cites W2146950091 @default.
- W1836715957 cites W2148831205 @default.
- W1836715957 cites W2151023586 @default.
- W1836715957 cites W2152761983 @default.
- W1836715957 cites W2153864077 @default.
- W1836715957 cites W2155513250 @default.
- W1836715957 cites W2155942458 @default.
- W1836715957 cites W2168046285 @default.
- W1836715957 cites W2168118654 @default.
- W1836715957 cites W2570764145 @default.
- W1836715957 cites W2911964244 @default.
- W1836715957 cites W2912934387 @default.
- W1836715957 cites W2964075712 @default.
- W1836715957 cites W3017143921 @default.
- W1836715957 cites W3112138688 @default.
- W1836715957 hasPublicationYear "2005" @default.
- W1836715957 type Work @default.
- W1836715957 sameAs 1836715957 @default.
- W1836715957 citedByCount "6" @default.
- W1836715957 countsByYear W18367159572012 @default.
- W1836715957 countsByYear W18367159572015 @default.
- W1836715957 countsByYear W18367159572020 @default.
- W1836715957 crossrefType "dissertation" @default.
- W1836715957 hasAuthorship W1836715957A5008715111 @default.
- W1836715957 hasAuthorship W1836715957A5038700517 @default.
- W1836715957 hasConcept C117765406 @default.