Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Data mining in finance: advances in relational and hybrid methods
Data mining in finance: advances in relational and hybrid methods
Multi-level organization and summarization of the discovered rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Growing decision trees on support-less association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Genetic Algorithms and Investment Strategies
Genetic Algorithms and Investment Strategies
Classifying text documents by associating terms with text categories
ADC '02 Proceedings of the 13th Australasian database conference - Volume 5
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
A Lazy Approach to Pruning Classification Rules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
On support thresholds in associative classification
Proceedings of the 2004 ACM symposium on Applied computing
An associative classifier based on positive and negative rules
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
A Novel Algorithm for Associative Classification of Image Blocks
CIT '04 Proceedings of the The Fourth International Conference on Computer and Information Technology
MMAC: A New Multi-Class, Multi-Label Associative Classification Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
A new approach to classification based on association rule mining
Decision Support Systems
MCAR: multi-class classification based on association rule
AICCSA '05 Proceedings of the ACS/IEEE 2005 International Conference on Computer Systems and Applications
A review of associative classification mining
The Knowledge Engineering Review
CSMC: A combination strategy for multi-class classification based on multiple association rules
Knowledge-Based Systems
A phenotypic genetic algorithm for inductive logic programming
Expert Systems with Applications: An International Journal
G-ANMI: A mutual information based genetic clustering algorithm for categorical data
Knowledge-Based Systems
Processing online analytics with classification and association rule mining
Knowledge-Based Systems
Evolutionary-based selection of generalized instances for imbalanced classification
Knowledge-Based Systems
Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
Fitness function based on binding and recall rate for genetic inductive logic programming
ICSI'12 Proceedings of the Third international conference on Advances in Swarm Intelligence - Volume Part I
A new relational Tri-training system with adaptive data editing for inductive logic programming
Knowledge-Based Systems
CAR-Miner: An efficient algorithm for mining class-association rules
Expert Systems with Applications: An International Journal
Improving classification accuracy of associative classifiers by using k-conflict-rule preservation
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
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Associative classifiers are a classification system based on associative classification rules. Although associative classification is more accurate than a traditional classification approach, it cannot handle numerical data and its relationships. Therefore, an ongoing research problem is how to build associative classifiers from numerical data. In this work, we focus on stock trading data with many numerical technical indicators, and the classification problem is finding sell and buy signals from the technical indicators. This study proposes a GA-based algorithm used to build an associative classifier that can discover trading rules from these numerical indicators. The experiment results show that the proposed approach is an effective classification technique with high prediction accuracy and is highly competitive when compared with the data distribution method.