A greedy classification algorithm based on association rule
Applied Soft Computing
A review of associative classification mining
The Knowledge Engineering Review
Mining the data from a hyperheuristic approach using associative classification
Expert Systems with Applications: An International Journal
An Efficient Association Rule Mining Algorithm for Classification
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Ml-rbf: RBF Neural Networks for Multi-Label Learning
Neural Processing Letters
A Novel Classification Algorithm Based on Association Rules Mining
Knowledge Acquisition: Approaches, Algorithms and Applications
Feature selection for multi-label naive Bayes classification
Information Sciences: an International Journal
Multi-label learning by instance differentiation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Mining Multi-label Concept-Drifting Data Streams Using Dynamic Classifier Ensemble
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
CIMDS: adapting postprocessing techniques of associative classification for malware detection
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Mining associative classification rules with stock trading data - A GA-based method
Knowledge-Based Systems
Multilabel dimensionality reduction via dependence maximization
ACM Transactions on Knowledge Discovery from Data (TKDD)
Intelligent phishing detection system for e-banking using fuzzy data mining
Expert Systems with Applications: An International Journal
Performance of NB and SVM classifiers in Islamic Arabic data
Proceedings of the 1st International Conference on Intelligent Semantic Web-Services and Applications
An approach for adaptive associative classification
Expert Systems with Applications: An International Journal
Categorize arabic data sets using multi-class classification based on association rule approach
Proceedings of the 2011 International Conference on Intelligent Semantic Web-Services and Applications
Two stage architecture for multi-label learning
Pattern Recognition
Multi-instance multi-label learning
Artificial Intelligence
Mining correlated rules for associative classification
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
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
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
An extensive experimental comparison of methods for multi-label learning
Pattern Recognition
I-prune: Item selection for associative classification
International Journal of Intelligent Systems
Learning tree structure of label dependency for multi-label learning
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Improving the performance of association classifiers by rule prioritization
Knowledge-Based Systems
CAR-Miner: An efficient algorithm for mining class-association rules
Expert Systems with Applications: An International Journal
Interestingness measures for classification based on association rules
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
A hybrid recommendation approach for a tourism system
Expert Systems with Applications: An International Journal
Iterative classification for multiple target attributes
Journal of Intelligent Information Systems
Sentiment and topic analysis on social media: a multi-task multi-label classification approach
Proceedings of the 5th Annual ACM Web Science Conference
ACNB: Associative Classification Mining Based on Naïve Bayesian Method
International Journal of Information Technology and Web Engineering
| Hi-index | 0.01 |
Building fast and accurate classifiers for large-scale databases is an important task in data mining. There is growing evidence that integrating classification and association rule mining together can produce more efficient and accurate classifiers than traditional classification techniques. In this paper, the problem of producing rules with multiple labels is investigated. We propose a new associative classification approach called multi-class, multi-label associative classification (MMAC). This paper also presents three measures for evaluating the accuracy of data mining classification approaches to a wide range of traditional and multi-label classification problems. Results for 28 different datasets show that the MMAC approach is an accurate and effective classification technique, highly competitive and scalable in comparison with other classification approaches.