The Strength of Weak Learnability
Machine Learning
Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
C4.5: programs for machine learning
C4.5: programs for machine learning
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Constructing X-of-N Attributes for Decision Tree Learning
Machine Learning
Machine Learning
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Geography of Differences between Two Classes of Data
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
An Efficient Single-Scan Algorithm for Mining Essential Jumping Emerging Patterns for Classification
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Essential classification rule sets
ACM Transactions on Database Systems (TODS)
Using Emerging Patterns to Construct Weighted Decision Trees
IEEE Transactions on Knowledge and Data Engineering
Boosting an Associative Classifier
IEEE Transactions on Knowledge and Data Engineering
Classifying Chemical Compounds Using Contrast and Common Patterns
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
CCIC: Consistent Common Itemsets Classifier
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Classification of Web Documents Using a Graph-Based Model and Structural Patterns
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Discovering Relational Emerging Patterns
AI*IA '07 Proceedings of the 10th Congress of the Italian Association for Artificial Intelligence on AI*IA 2007: Artificial Intelligence and Human-Oriented Computing
A Novel Algorithm for Associative Classification
Neural Information Processing
Emerging Pattern Based Classification in Relational Data Mining
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Using Highly Expressive Contrast Patterns for Classification - Is It Worthwhile?
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Credibility Coefficients in Hybrid Artificial Intelligence Systems
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Ordinal Credibility Coefficient --- A New Approach in the Data Credibility Analysis
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Constructing Associative Classifier Using Rough Sets and Evidence Theory
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Mining Class Contrast Functions by Gene Expression Programming
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
EP-based robust weighting scheme for fuzzy SVMs
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
Transactions on rough sets XII
On the stimulation of patterns: definitions, calculation method and first usages
ICCS'10 Proceedings of the 18th international conference on Conceptual structures: from information to intelligence
Mining contrast inequalities in numeric dataset
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Building a highly-compact and accurate associative classifier
Applied Intelligence
Mining Layered Grammar Rules for Action Recognition
International Journal of Computer Vision
An approach for adaptive associative classification
Expert Systems with Applications: An International Journal
Efficient mining of jumping emerging patterns with occurrence counts for classification
Transactions on rough sets XIII
PolyA-iEP: A data mining method for the effective prediction of polyadenylation sites
Expert Systems with Applications: An International Journal
A simple statistical model and association rule filtering for classification
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Enumeration tree based emerging patterns mining by using two different supports
ICHIT'11 Proceedings of the 5th international conference on Convergence and hybrid information technology
Condensed representation of EPs and patterns quantified by frequency-based measures
KDID'04 Proceedings of the Third international conference on Knowledge Discovery in Inductive Databases
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
DRC-BK: mining classification rules by using Boolean kernels
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and its Applications - Volume Part I
Further improving emerging pattern based classifiers via bagging
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Deriving class association rules based on levelwise subspace clustering
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
ACME: an associative classifier based on maximum entropy principle
ALT'05 Proceedings of the 16th international conference on Algorithmic Learning Theory
Adjusting and generalizing CBA algorithm to handling class imbalance
Expert Systems with Applications: An International Journal
A hierarchical approach to real-time activity recognition in body sensor networks
Pervasive and Mobile Computing
OSDM: optimized shape distribution method
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Classification with maximum entropy modeling of predictive association rules
ECML'05 Proceedings of the 16th European conference on Machine Learning
Mutagenicity risk analysis by using class association rules
JSAI'05 Proceedings of the 2005 international conference on New Frontiers in Artificial Intelligence
Succinct and informative cluster descriptions for document repositories
WAIM '06 Proceedings of the 7th international conference on Advances in Web-Age Information Management
Building a more accurate classifier based on strong frequent patterns
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Exploiting maximal emerging patterns for classification
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Efficiently finding the best parameter for the emerging pattern-based classifier PCL
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Hiding emerging patterns with local recoding generalization
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
I-prune: Item selection for associative classification
International Journal of Intelligent Systems
Mining emerging patterns by streaming feature selection
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental set recommendation based on class differences
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Expert Systems with Applications: An International Journal
Pattern-based real-time feedback for a temporal bone simulator
Proceedings of the 19th ACM Symposium on Virtual Reality Software and Technology
Editorial: Parameter-free classification in multi-class imbalanced data sets
Data & Knowledge Engineering
Identifying risky environments for COPD patients using smartphones and internet of things objects
International Journal of Computational Intelligence Studies
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Emerging patterns (EPs) are itemsets whose supports change significantly from one dataset to another; they were recently proposed to capture multi-attribute contrasts between data classes, or trends over time. In this paper we propose a new classifier, CAEP, using the following main ideas based on EPs: (i) Each EP can sharply differentiate the class membership of a (possibly small) fraction of instances containing the EP, due to the big difference between its supports in the opposing classes; we define the differentiating power of the EP in terms of the supports and their ratio, on instances containing the EP. (ii) For each instance t, by aggregating the differentiating power of a fixed, automatically selected set of EPs, a score is obtained for each class. The scores for all classes are normalized and the largest score determines t's class. CAEP is suitable for many applications, even those with large volumes of high (e.g. 45) dimensional data; it does not depend on dimension reduction on data; and it is usually equally accurate on all classes even if their populations are unbalanced. Experiments show that CAEP has consistent good predictive accuracy, and it almost always outperforms C4.5 and CBA. By using efficient, border-based algorithms (developed elsewhere) to discover EPs, CAEP scales up on data volume and dimensionality. Observing that accuracy on the whole dataset is too coarse description of classifiers, we also used a more accurate measure, sensitivity and precision, to better characterize the performance of classifiers. CAEP is also very good under this measure.