Title of Thesis

Classification and Associative Classification Rule Discovery Using Ant Colony Optimization

Author(s)

Waseem Shahzad

Abstract
The primary goal of this research is to investigate the suitability of ant colony optimization, a swarm intelligence based meta-heuristic developed by mimicking some aspects of the food foraging behavior of ants, for building accurate and comprehensible classifiers which can be learned in reasonable time even for large datasets. Towards this end, a novel classification rule discovery algorithm called AntMiner-C and its variants are proposed.Various aspects and parameters of the proposed algorithms are investigated by experimentation on a number of benchmark datasets. Experimental results indicate that the proposed approach builds more accurate models when compared with commonly used classification algorithms.It is also computationally less expensive than previously available ant colony algorithm based classification rules discovery algorithms.
A hybrid classifier using ant colony optimization is also proposed that combines association rules mining and supervised classification. Experiments show that the proposed algorithm has the ability to discover high quality rules. Furthermore, it has the advantage that association rules of each class can be mined in parallel if distributed processing is used. Experimental results demonstrate that the proposed hybrid classifier achieves higher accuracy rates when compared with other commonly used classification algorithms.
A feature subset selection algorithm is also proposed which is based on ant colony optimization and decision trees.Experiments show that better accuracy is achieved if the subset of features selected by the proposed approach is used instead of full feature set and number of rules is also decreased substantially.

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1.1 Research Background
1.2 Research Contributions
1.3 Layout of Thesis

2.1 Classification
2.2 Performance Evaluation of Classification Methods
2.3 Types of Classifiers
2.4 Summary

3.1 Swarm intelligence
3.2 Ant Colony Optimization
3.3 ACO Based Classification Rule Discovery: AntMiner Algorithms
3.4 Summary

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4.1 Introduction
4.2 Correlation Based AntMiner (AntMiner–C)
4.3 Summary

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5.1 Experiments and Analysis
5.2 Analysis of Different Algorithmic Components
5.3 Improved AntMiner-C
5.4 Parameter Optimization
5.5 Results and Comparisons
5.6 Time Complexity of AntMiner-C
5.7 Summary

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6.1 Modified Algorithm: AntMiner-CC
6.2 Differences with previous versions
6.3 Heuristic Function of AntMiner-CC
6.4 Default Rule
6.5 Experiments and Analysis
6.6 Comparison with Other Algorithms
6.7 Summary

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7.1 Associative Rules Mining and Associative Classification
7.2 Differences with AntMiner-C and AntMiner-CC
7.3 Proposed Technique
7.4 Experiments and Analysis
7.5 Time Complexity of ACO-AC
7.6 Summary

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8.1 Introduction
8.2 Decision Trees
8.3 Proposed Technique
8.4 Experimentation and Analysis
8.5 Summary

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9.1 Conclusion
9.2 Future Work
 

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