Instance-Based Learning Algorithms
Machine Learning
A Nearest Hyperrectangle Learning Method
Machine Learning
Selecting typical instances in instance-based learning
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
A hybrid nearest-neighbor and nearest-hyperrectangle algorithm
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Machine Learning
Unifying instance-based and rule-based induction
Machine Learning
Lazy learning
Machine Learning - Special issue on learning with probabilistic representations
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
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Extending naïve Bayes classifiers using long itemsets
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Interestingness via what is not interesting
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Making use of the most expressive jumping emerging patterns for classification
Knowledge and Information Systems
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
The Space of Jumping Emerging Patterns and Its Incremental Maintenance Algorithms
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Interestingness of Discovered Association Rules in Terms of Neighborhood-Based Unexpectedness
PAKDD '98 Proceedings of the Second Pacific-Asia Conference on Research and Development in Knowledge Discovery and Data Mining
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Voting Nearest-Neighbor Subclassifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Incremental Maintenance on the Border of the Space of Emerging Patterns
Data Mining and Knowledge Discovery
Boosting an Associative Classifier
IEEE Transactions on Knowledge and Data Engineering
Training a reciprocal-sigmoid classifier by feature scaling-space
Machine Learning
On Mining Instance-Centric Classification Rules
IEEE Transactions on Knowledge and Data Engineering
RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning
Fundamenta Informaticae
World Wide Web
Customized classification learning based on query projections
Information Sciences: an International Journal
An error-counting network for pattern classification
Neurocomputing
Deterministic neural classification
Neural Computation
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
Emerging Pattern Based Classification in Relational Data Mining
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Mining Class Contrast Functions by Gene Expression Programming
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
ACIK: association classifier based on itemset kernel
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Emerging patterns based methodology for prediction of patients with myocardial ischemia
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Contrast pattern mining and its applications
ADC '10 Proceedings of the Twenty-First Australasian Conference on Database Technologies - Volume 104
Transactions on rough sets XII
Mining contrast inequalities in numeric dataset
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Efficient mining of jumping emerging patterns with occurrence counts for classification
Transactions on rough sets XIII
Local reducts and jumping emerging patterns in relational databases
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Local pattern discovery in Array-CGH data
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
Building a more accurate classifier based on strong frequent patterns
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Analogy-based reasoning in classifier construction
Transactions on Rough Sets IV
An efficient mining algorithm for maximal weighted frequent patterns in transactional databases
Knowledge-Based Systems
I-prune: Item selection for associative classification
International Journal of Intelligent Systems
RIONA: A New Classification System Combining Rule Induction and Instance-Based Learning
Fundamenta Informaticae
On the Evolution of Hardware Circuits via Reconfigurable Architectures
ACM Transactions on Reconfigurable Technology and Systems (TRETS)
An evolutionary method for associative local distribution rule mining
ICDM'13 Proceedings of the 13th international conference on Advances in Data Mining: applications and theoretical aspects
Editorial: Parameter-free classification in multi-class imbalanced data sets
Data & Knowledge Engineering
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Distance is widely used in most lazy classification systems. Rather than using distance, we make use of the frequency of an instance's subsets of features and the frequency-change rate of the subsets among training classes to perform both knowledge discovery and classification. We name the system DeEPs. Whenever an instance is considered, DeEPs can efficiently discover those patterns contained in the instance which sharply differentiate the training classes from one to another. DeEPs can also predict a class label for the instance by compactly summarizing the frequencies of the discovered patterns based on a view to collectively maximize the discriminating power of the patterns. Many experimental results are used to evaluate the system, showing that the patterns are comprehensible and that DeEPs is accurate and scalable.