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Learning with Rare Cases and Small Disjuncts
[chapter]
1995
Machine Learning Proceedings 1995
Preserved Fulltext
Acknowledgements I would like to thank Andrea Danyluk and Rob Holte for their comments on an earlier version of this paper, and Foster Provost, Brian Davison, and Rosalie DiSimone-Weiss for comments on ...
I would especially like to thank Haym Hirsh for valuable early discussions and many helpful comments. ...
Rare cases tend to cause small disjuncts to be formed during learning. ...
doi:10.1016/b978-1-55860-377-6.50075-x
dblp:conf/icml/Weiss95
fatcat:c6edwguklverhetoobz24yvt3i
Mining with rarity
2004
SIGKDD Explorations
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This article discusses the role that rare classes and rare cases play in data mining. ...
This article also demonstrates that rare classes and rare cases are very similar phenomena-both forms of rarity are shown to cause similar problems during data mining and benefit from the same remediation ...
on the impact of small disjuncts and class distribution on data mining. ...
doi:10.1145/1007730.1007734
fatcat:sb2tk62wrffifcirx75kw22etq
Foundations of Imbalanced Learning
[chapter]
2013
Imbalanced Learning
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It then describes the fundamental learning issues that arise when learning from imbalanced data, and categorizes these issues as ...
This chapter begins by describing what is meant by imbalanced data, and by showing the effects of such data on learning. ...
positively-labeled cases, one a rare case and one a common case, and the small disjunct and large disjunct that the classifier forms to cover them. ...
doi:10.1002/9781118646106.ch2
fatcat:opqe7dy2onaadp2ckacz6bdaxq
Learning with Class Skews and Small Disjuncts
[chapter]
2004
Lecture Notes in Computer Science
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Our results suggest that these methods are effective for dealing with class imbalance and, in some cases, might help in ruling out some undesirable disjuncts. ...
In this sense, this work analyzes two important issues that might influence the performance of ML systems: class imbalance and errorprone small disjuncts. ...
This research was partially supported by the Brazilian Research Councils CAPES and FAPESP. ...
doi:10.1007/978-3-540-28645-5_30
fatcat:g5quowqhnzczbclglwhjgz44mm
The Impact of Small Disjuncts on Classifier Learning
[chapter]
2009
Annals of Information Systems
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Various factors, including pruning, training-set size, noise and class imbalance are then analyzed to determine how they affect small disjuncts and the distribution of errors across disjuncts. ...
This analysis provides many insights into why some data sets are difficult to learn from and also provides a better understanding of classifier learning in general. ...
cases and causing the wrong subconcept to be learned The majority of research on small disjuncts focuses on ways to address the problem with small disjuncts. ...
doi:10.1007/978-1-4419-1280-0_9
fatcat:3uooy4k4pjawxl4ud25cs7eey4
Evolutionary rule-based systems for imbalanced data sets
2008
Soft Computing - A Fusion of Foundations, Methodologies and Applications
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When LCSs are used with real-world problems, they demonstrate to be one of the most robust methods compared with instance-based learners, decision trees, and support vector machines. ...
While some learners may suffer from class imbalances and instances sparsely distributed around the feature space, we show that LCSs are flexible methods that can be adapted to detect such cases and find ...
de Catalunya under Grants 2005FI-00252 and 2005SGR-00302. ...
doi:10.1007/s00500-008-0319-7
fatcat:iver4ubrkfe2nilt27bcdescxq
An Evolutionary Algorithm for Automated Discovery of Small-Disjunct Rules
2012
International Journal of Computer Applications
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of small disjuncts. ...
The proposed algorithm is validated on several datasets of UCI data set repository and the experimental results are presented to demonstrate the effectiveness of the proposed scheme for automated small-disjunct ...
They argue with justification that learned concepts must be able to include small disjuncts arising from exceptions and rare cases. ...
doi:10.5120/5547-7615
fatcat:zz4ecdvslbgedef6y7qh2gv3dy
Enhanced Classification to Counter the Problem of Cluster Disjuncts
English
2014
International Journal of Computer Trends and Technology
English
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This algorithm provides a simpler and faster alternative by using cluster disjunct concept. ...
This paper presets a rigorous yet practical model dubbed as Cluster Disjunct Minority Oversampling Technique (CDMOTE) for learning from skewed training data. ...
Moreover, since classifiers attempt to learn both majority and minority a concept, the problem of small disjuncts is not only restricted to the minority concept. ...
doi:10.14445/22312803/ijctt-v18p148
fatcat:nvzqkn54z5fa5hmzyeswjefvi4
Improved Estimates for the Accuracy of Small Disjuncts
1991
Machine Learning
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However, experiments show that small disjuncts associated with target classes of different relative frequencies tend to have different error rates. ...
Trials are reported comparing the performance of the original formula and the adaptation in six learning tasks. ...
Acknowledgments Thanks to Teddy Seidenfeld, Rob Holte, and Tom Dietterich for their input. The Machine Learning List, moderated by Mike Pazzani, also provided a useful forum for airing the problem. ...
doi:10.1023/a:1022646118217
dblp:journals/ml/Quinlan91
fatcat:dnflkhgn2rgxjaox2ktf4eyej4
Improved estimates for the accuracy of small disjuncts
1991
Machine Learning
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However, experiments show that small disjuncts associated with target classes of different relative frequencies tend to have different error rates. ...
Trials are reported comparing the performance of the original formula and the adaptation in six learning tasks. ...
Acknowledgmen~ Thanks to Teddy Seidenfeld, Rob Holte, and Tom Dietterich for their input. The Machine Learning List, moderated by Mike Pazzani, also provided a useful forum for airing the problem. ...
doi:10.1007/bf00153762
fatcat:gjhokjcqovbf5fjokn6pyhotd4
Editorial
2004
SIGKDD Explorations
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We would also like to thank the participants and attendees of the previous workshops for the enlightening presentations and discussions. ...
ACKNOWLEDGEMENTS We thank the reviewers for their useful and timely comments on the papers submitted to this Issue. ...
A number of papers discussed interaction between the class imbalance and other issues such as the small disjunct [27] and the rare cases [23] problems, data duplication [34] , and overlapping classes ...
doi:10.1145/1007730.1007733
fatcat:tdpfkg6vgbgqrpjtclkhrz5rne
A Novel Imbalanced Classification Method based on Decision Tree and Bagging
2018
International Journal of Performability Engineering
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Complex data distributions, such as small disjuncts and overlapping classes, make traditional methods unable to easily recognize the minority class and thus, lead to low sensitivity. ...
Experimental results demonstrate that OEBag performs significantly better in sensitivity and has a great overall performance in terms of AUC (area under ROC curve) and G-mean when compared with several ...
Complex distributions mainly contain small disjuncts [10] , overlapping classes [16] , and too many rare cases and outliers [14] in the minority class space. ...
doi:10.23940/ijpe.18.06.p5.11401148
fatcat:pwvgv2lwgvbfrhlq5xeqwgdxsi
A genetic-algorithm for discovering small-disjunct rules in data mining
2002
Applied Soft Computing
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This paper presents evidence that this is the case in several data sets. This paper also addresses the problem of small disjuncts by using a hybrid decision-tree/genetic algorithm approach. ...
However, although each small disjunct covers few examples, the set of all small disjuncts can cover a large number of examples. ...
Weiss investigated the interaction of noise with rare cases (true exceptions) and showed that this interaction led to degradation in classification accuracy when small-disjunct rules are eliminated [22 ...
doi:10.1016/s1568-4946(02)00031-5
fatcat:av7ifipllng7jbbt7ppdfo7vvu
Overlapping, Rare Examples and Class Decomposition in Learning Classifiers from Imbalanced Data
[chapter]
2013
Smart Innovation, Systems and Technologies
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Results show that if data is sufficiently disturbed by borderline and rare examples SPIDER and partly NCR work better than over-sampling. ...
Class imbalance constitutes a difficulty for most algorithms learning classifiers as they are biased toward the majority classes. ...
Small disjuncts are these parts of the learned classifier which cover a too small number of examples [20, 62] . ...
doi:10.1007/978-3-642-28699-5_11
fatcat:42bihx3adndylpj742o2pwkhg4
Concept Learning and the Problem of Small Disjuncts
1989
International Joint Conference on Artificial Intelligence
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The problem with small disjuncts is that many of them have high rates of misclassification, and it is difficult to eliminate the errorprone small disjuncts from a definition without adversely affecting ...
Existing inductive systems create definitions that are ideal with regard to large disjuncts, but far from ideal with regard to small disjuncts, where a small (large) disjunct is one that correctly classifies ...
Peter also answered many questions, and gave helpful criticism of early drafts of this paper. ...
dblp:conf/ijcai/HolteAP89
fatcat:tgbtjcr64nf6vmpe7osvxumneu
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