02257nas a2200205   4500008004100000245006800041210006600109260000900175300001200184490000700196520160100203653001901804653002701823653001901850653002701869100001301896700001301909700001201922856011701934       2012                        eng d00aCMCCR: Classification based on multiple class-correlation rules0 aCMCCR Classification based on multiple classcorrelation rules  c2012  a64 - 700 v103 aPrevious studies show that associative classification achieves higher classification accuracy than traditional classification approaches. However, associative classification suffers from two major deficiencies and thus sometimes has lower classification accuracy. First, it often generates a large number of rules when the minimum support is set to be low. It is not only time consuming to discover so many itemsets, but also very difficult to select high quality rules from a huge set of rules. Second, the association measure confidence does not reflect correlation relationships between itemsets and the class label. To deal these problems, in this paper, we propose a new classification approach called classification based on multiple class-correlation rules (CMCCR). First, we use both support and all-confidence to mine not only frequent but also mutually associated itemsets. This method not only saves the running time but as well considerably reduces the number of itemsets generated, and thus sharply decreases the number of class-correlation rules produced. Second, we use a new correlation measure correlation confidence to discover class-correlation rules. The measure correlation confidence has two bounds:- 1and 1. It is easy to control the correlation degree of the class-correlation rules found. Finally, we use multiple class-correlation rules and average correlation degree to measure the combined effect of group rules. Experimental results on the mushroom data set show that CMCCR has higher and more stable accuracy than associative classification and decision tree method.10aAll-confidence10aClass-correlation rule10aClassification10aCorrelation confidence1 aZhou, Z.1 aWang, X.1 aPan, G.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84866448185&partnerID=40&md5=10fb4c8ff12d02f2555fb4ba4c8f5d0401389nas a2200217   4500008004100000245007100041210006700112260000900179300001100188490000600199520068400205653001900889653002200908653001900930653002700949653002600976100001501002700001601017700002101033856011701054       2011                        eng d00aAn ontology-based approach for detecting knowledge intensive tasks0 aontologybased approach for detecting knowledge intensive tasks  c2011  a9 - 180 v93 aIn the context detection field, an important challenge is automatically detecting the user's task, for providing contextualized and personalized user support. Several approaches have been proposed to perform task classification, all advocating the window title as the best discriminative feature. In this paper we present a new ontology-based task detection approach, and evaluate it against previous work. We show that knowledge intensive tasks cannot be accurately classified using only the window title. We argue that our approach allows classifying such tasks better, by providing feature combinations that can adapt to the domain and the degree of freedom in task execution.10aClassification10aContext-awareness10aTask detection10aUser context detection10aUser context ontology1 aRath, A.S.1 aDevaurs, D.1 aLindstaedt, S.N.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960684788&partnerID=40&md5=2680764b7de9a96a3bfc50e670a65ce801812nas a2200193   4500008004100000245006400041210006400105260000900169300001400178490000600192520118900198653002001387653001901407653002401426653002001450100001601470700001501486856011701501       2010                        eng d00aSystem of boosting voting with multiple learning algorithms0 aSystem of boosting voting with multiple learning algorithms  c2010  a153 - 1590 v83 aThe Ensemble classifier has been an active research topic in the area of machine learning. In a classification task, the ensemble scheme determines a final class label from several individual results which are usually generated by several individual classifiers according to two major principles. The first is to use multiple learning algorithms to form the set of individual outcomes, and the second is to train a set of data fractions, which are generated from a given training dataset, with a weak learning algorithm to generate the set of results. The advantage of the first principle is on classification stability, whereas that of the second principle is on the accuracy improvement in terms of classification accuracy. Thus far, most studies in the literature have been based on either a study of the two principles of ensemble. In this paper we propose a new ensemble scheme to combine the two principles simultaneously. We evaluate the performance on a classification task. Experimental results on several UCI benchmark datasets demonstrate that the proposed framework achieves improved performance in terms of classification accuracy compared to the conventional approaches.10aBoosting voting10aClassification10aEnsemble classifier10aMajority voting1 aChen, C.-H.1 aHsu, C.-C.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960667758&partnerID=40&md5=c1ce08210de8719732261454255800d901850nas a2200193   4500008004100000245007600041210006900117260000900186300001400195490000600209520120400215653002101419653003101440653001901471653001601490653001701506100001601523856011701539       2006                        eng d00aPruning techniques in associative classification: Survey and comparison0 aPruning techniques in associative classification Survey and comp  c2006  a197 - 2020 v43 aAssociation rule discovery and classification are common data mining tasks. Integrating association rule and classification also known as associative classification is a promising approach that derives classifiers highly competitive with regards to accuracy to that of traditional classification approaches such as rule induction and decision trees. However, the size of the classifiers generated by associative classification is often large and therefore pruning becomes an essential task. In this paper, we survey different rule pruning methods used by current associative classification techniques. Further, we compare the effect of three pruning methods (database coverage, pessimistic error estimation, lazy pruning) on the accuracy rate and the number of rules derived from different classification data sets. Results obtained from experimenting on different data sets from UCI data collection indicate that lazy pruning algorithms may produce slightly higher predictive classifiers than those which utilise database coverage and pessimistic error pruning methods. However, the potential use of such classifiers is limited because they are difficult to understand and maintain by the end-user.10aAssociation rule10aAssociative classification10aClassification10aData mining10aRule pruning1 aThabtah, F.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-33749400880&partnerID=40&md5=0633368b3744eefc60e6e205c64d95f4