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21 results on '"Raju, Nedunchezhian"'

17. A Fast Boosting Based Incremental Genetic Algorithm for Mining Classification Rules in Large Datasets

Author

Periasamy Vivekanandan and Raju Nedunchezhian

Abstract

Genetic algorithm is a search technique purely based on natural evolution process. It is widely used by the data mining community for classification rule discovery in complex domains. During the learning process it makes several passes over the data set for determining the accuracy of the potential rules. Due to this characteristic it becomes an extremely I/O intensive slow process. It is particularly difficult to apply GA when the training data set becomes too large and not fully available. An incremental Genetic algorithm based on boosting phenomenon is proposed in this paper which constructs a weak ensemble of classifiers in a fast incremental manner and thus tries to reduce the learning cost considerably.

Published
2013

18. Maximal frequent itemset generation using segmentation approach

Author

T. Purusothaman, M. Rajalakshmi, and Raju Nedunchezhian

Subjects
FOS: Computer and information sciences, Data source, Prioritization, Association rule learning, Computer science, InformationSystems_DATABASEMANAGEMENT, Databases (cs.DB), Subset and superset, computer.software_genre, ComputingMethodologies_PATTERNRECOGNITION, Computer Science - Databases, ComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION, Segmentation, Data mining, computer
Abstract

Finding frequent itemsets in a data source is a fundamental operation behind Association Rule Mining. Generally, many algorithms use either the bottom-up or top-down approaches for finding these frequent itemsets. When the length of frequent itemsets to be found is large, the traditional algorithms find all the frequent itemsets from 1-length to n-length, which is a difficult process. This problem can be solved by mining only the Maximal Frequent Itemsets (MFS). Maximal Frequent Itemsets are frequent itemsets which have no proper frequent superset. Thus, the generation of only maximal frequent itemsets reduces the number of itemsets and also time needed for the generation of all frequent itemsets as each maximal itemset of length m implies the presence of 2m-2 frequent itemsets. Furthermore, mining only maximal frequent itemset is sufficient in many data mining applications like minimal key discovery and theory extraction. In this paper, we suggest a novel method for finding the maximal frequent itemset from huge data sources using the concept of segmentation of data source and prioritization of segments. Empirical evaluation shows that this method outperforms various other known methods., 14 pages

Published
2011

19. A Greedy Approach for Coverage-Based Test Suite Reduction.

Author

Harris, Preethi and Raju, Nedunchezhian

Subjects
COMPUTER software testing, ERROR analysis in mathematics, DATA flow computing, COMPUTER algorithms, NETWORK performance
Abstract

Software testing is an activity to find maximum number of errors which have not been discovered yet with optimum time and effort. As the software evolves the size of the test suite and grows with new test cases being added to the test suite. However, due to time and resource constraints rerunning all the test cases in the test suite is not possible, every time the software is modified, in order to deal with these issues, the test suite size should be manageable. In this paper a novel approach is presented to select a subset of test cases that exercise the given set of requirements with for data flow testing. In order to, express the effectiveness of the proposed algorithm, both the existing Harrold Gupta and Soffa (HGS) and Bi-Objective Greedy (BOG) algorithms were applied to the generated test suites. The results obtained from the proposed algorithm were compared with the state-of-art algorithms. The results of the performance evaluation, when compared to the existing approaches show that, the proposed algorithm selects near optimal test cases that satisfy maximum number of testing requirements without compromising on the coverage aspect. [ABSTRACT FROM AUTHOR]

Published
2015

20. Empowering artificial intelligence through machine learning : new advances and applications.

Author

Raju, Nedunchezhian, Rajalakshmi, M., Goyal, Dinesh, Balamurugan, S., Prof, Elngar, Ahmed A., and Keswani, Bright

Subjects
Machine learning, Artificial intelligence -- Industrial applications
Abstract

Summary: "This new volume, Empowering Artificial Intelligence Through Machine Learning: New Advances and Applications, discusses various new applications of machine learning, a subset of the field of artificial intelligence. Artificial intelligence is considered to be the next big-game changer in research and technology. The volume looks at how computing has enabled machines to learn, making machines and tools become smarter in many sectors, including science and engineering, healthcare, finance, education, gaming, security, and even agriculture, plus many more areas. Topics include techniques and methods in artificial intelligence for making machines intelligent, machine learning in healthcare, using machine learning for credit card fraud detection, using artificial intelligence in education using gaming and automatization with courses and outcomes mapping, and much more. The book will be valuable for professionals, faculty, and students in electronics and communication engineering, telecommunication engineering, network engineering, computer science and information technology"-- Provided by publisher.

Published
2022

21. A Graph Based Heuristic Towards Test Suite Reduction.

Author

Harris, Preethi and Raju, Nedunchezhian

Subjects
GRAPH theory, COMPUTER software, ALGORITHMS, DATA flow computing, COMPARATIVE studies, AUTOMATIC control systems, EMPIRICAL research
Abstract

Maintaining test suites for testing software can become increasingly difficult as test suite sizes grow over time. Test suite minimization techniques are therefore used, to remove the test cases from a test suite that have become redundant and obsolete with respect to a particular coverage criterion. However, minimizing a test suite without considering a coverage criterion may compromise the fault detection effectiveness of the test suite. To address this issue, kruskal's algorithm has been adapted to construct the representative set with respect to data flow coverage criterion, while retaining a high percentage of the original test suite's fault detection effectiveness. The proposed approach has been evaluated with the classical HGS and recently proposed BOG algorithms. Empirical results obtained, show that compared to existing test suite minimization techniques, the proposed approach has a strong tendency to improve the coverage aspect of the reduced test suites without significantly affecting the fault detection effectiveness. [ABSTRACT FROM AUTHOR]

Published
2012

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