@article {323,
	title = {CMCCR: Classification based on multiple class-correlation rules},
	journal = {Journal of Digital Information Management},
	volume = {10},
	year = {2012},
	note = {Export Date: 10 July 2014},
	month = {2012},
	pages = {64 - 70},
	abstract = {Previous 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.},
	keywords = {All-confidence, Class-correlation rule, Classification, Correlation confidence},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84866448185\&partnerID=40\&md5=10fb4c8ff12d02f2555fb4ba4c8f5d04},
	author = {Zhou, Z. and Wang, X. and Pan, G.}
}
@article {263,
	title = {An ontology-based approach for detecting knowledge intensive tasks},
	journal = {Journal of Digital Information Management},
	volume = {9},
	year = {2011},
	note = {Cited By (since 1996):2Export Date: 10 July 2014},
	month = {2011},
	pages = {9 - 18},
	abstract = {In the context detection field, an important challenge is automatically detecting the user{\textquoteright}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.},
	keywords = {Classification, Context-awareness, Task detection, User context detection, User context ontology},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960684788\&partnerID=40\&md5=2680764b7de9a96a3bfc50e670a65ce8},
	author = {Rath, A.S. and Devaurs, D. and Lindstaedt, S.N.}
}
@article {230,
	title = {System of boosting voting with multiple learning algorithms},
	journal = {Journal of Digital Information Management},
	volume = {8},
	year = {2010},
	note = {Export Date: 10 July 2014},
	month = {2010},
	pages = {153 - 159},
	abstract = {The 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.},
	keywords = {Boosting voting, Classification, Ensemble classifier, Majority voting},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960667758\&partnerID=40\&md5=c1ce08210de8719732261454255800d9},
	author = {Chen, C.-H. and Hsu, C.-C.}
}
@article {55,
	title = {Pruning techniques in associative classification: Survey and comparison},
	journal = {Journal of Digital Information Management},
	volume = {4},
	year = {2006},
	note = {Cited By (since 1996):6Export Date: 10 July 2014},
	month = {2006},
	pages = {197 - 202},
	abstract = {Association 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.},
	keywords = {Association rule, Associative classification, Classification, Data mining, Rule pruning},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-33749400880\&partnerID=40\&md5=0633368b3744eefc60e6e205c64d95f4},
	author = {Thabtah, F.}
}
