Adaptive Firefly Optimization on Reducing High Dimensional Weighted Word Affinity Graph

Authors

  • Yadhav P D.A.V. College of Engineering and Technology, India

Keywords:

firefly, adaptive, dimensionality, semantic, information, retrieval

Abstract

Document analysis and retrieval system can best define an efficient information retrieval system. Among various processing stages in a document analysis and retrieval system, feature descriptors at processing volume limit require more importance while developing the system. This is mainly because of the increase in probability of getting high dimensional semantic description. This increases the vitality of opting a robust dimensionality reduction method for our retrieval system. Principle Component Analysis (PCA), Independent Component Analysis (ICA), etc are the most popular dimensionality reduction methods. However, they are highly complex while handling nonlinear data with multiple characteristics. Optimization algorithms can be a good alternative for the traditional methods. In fact, classical optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), etc have been widely applied. However, the data handling remains inefficient under current data exploding scenario. In our previous work, we have exploited Firefly Algorithm (FA) to solve the optimization problem. Due to parameter selection dilemma in traditional FA, this paper concentrates on using Adaptive Firefly Algorithm (AFA). AFA adaptively varies step search of solutions and hence improves the convergence rate of the algorithm. As a result, near – optimal solution can be obtained qualitatively. We further recommend the dimensionality reduction method to handle weighted word affinity graph to improve the retrieval efficiency.

References

Song Mao, Azriel Rosenfeld, Tapas Kanungo, “Document structure analysis algorithms: a literature survey”, DRR 2003, 2003, p.p. 197-

Carsten Gorg, Zhicheng Liu, Jaeyeon Kihm, Jaegul Choo, Haesun Park, Member, and John Stasko, “Combining Computational Analyses and Interactive Visualization for Document Exploration and Sensemaking in Jigsaw”, IEEE Transactions on Visualization and Computer Graphics, Vol. 19, No. 10, 2013, p.p. 1646 – 1663.

Jinxi Xu Amherst, W. Bruce Croft, “Query expansion using local and global document analysis”, Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, 1996, p.p. 4-11.

G. Salton, M. McGill, Eds. “Introduction to Modern Information Retrieval”, New York: McGraw-Hill, 1983.

S. Deerwester and S. Dumais, “Indexing by latent semantic analysis,” J. Amer. Soc. Inf. Sci., vol. 41, no. 6, 1990, pp. 391–407.

Haijun Zhang, John K. L. Ho, Q. M. Jonathan Wu, and Yunming Ye, “Multidimensional Latent Semantic Analysis Using Term Spatial Information”, IEEE Transactions on Cybernetics, Vol. 43, No. 6, 2013, p.p. 1625- 1640.

W. B. Frakes and R. Baeza-Yates, “Information Retrieval: Data Structures and Algorithms”, Prentice-Hall, Englewood Cliffs, NJ, 1992.

Antoniol, G. ; Canfora, G. ; Casazza, G. ; De Lucia, A; “Information retrieval models for recovering traceability links between code and

documentation”, Proceedings of International Conference on Software Maintenance, 2000, p.p. 40-49.

Yu-Gang Jiang ; Yang, J. ; Chong-Wah Ngo ; Hauptmann, A.G.;“Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study”, IEEE Transactions on Multimedia, Vol. 12, No. 1, Jan. 2010, p.p. 42 – 53.

Eaddy, M. ; Antoniol, G. ; Gueheneuc, Y.-G., “CERBERUS: Tracing Requirements to Source Code Using Information Retrieval, Dynamic Analysis, and Program Analysis”, 16th IEEE International Conference on Program Comprehension (ICPC 2008), 10-13 June 2008, p.p. 53 – 62.

G. Antoniol, G. Canfora, G. Casazza, A. De Lucia, E. Merlo, "Recovering Traceability Links between Code and Documentation," IEEE Transactions on Software Engineering, Vol .28, No. 10, 2002, p.p.970–983.

D. Poshyvanyk, Y.-G. Guéhéneuc, A. Marcus, G. Antoniol, V. Rajlich, "Feature Location Using Probabilistic Ranking of Methods Based on Execution Scenarios and Information Retrieval," IEEE Transactions on Software Engineering, Vol. 33, No. 6, 2007, p.p.420–432.

Akiko Aizawa, “An information-theoretic perspective of tf–idf measures”, Information Processing and Management, Vol. 39, 2003,p.p. 45–65.

Wray Buntine and Aleks Jakulin, “Applying discrete PCA in data analysis”, Proceedings of the 20th conference on Uncertainty in artificial intelligence, 2004, p.p. 59-66.

Yang, X. S. (2008). Nature-Inspired Metaheuristic Algorithms. Frome: Luniver Press. ISBN 1-905986-10-6.

Taiping Zhang; Yuan Yan Tang; Bin Fang; Yong Xiang,“Document Clustering in Correlation Similarity Measure Space”,IEEE Transactions on Knowledge and Data Engineering, Vol. 24, No. 6, p.p. 1002 – 1013, 2012.

Zhang, L. ; Zhao, Y. ; Zhu, Z. ; Wei, S. ; Wu, X. “Mining Semantically Consistent Patterns for Cross-View Data”, IEEE Transactions on Knowledge and Data Engineering, Vol: 26, No. 11, p.p. 2745- 2758, 2014.

Chen, B. ; Kuan-Yu Chen ; Pei-Ning Chen ; Yi-Wen Chen, “Spoken Document Retrieval With Unsupervised Query Modeling Techniques”, IEEE Transactions on Audio, Speech, and Language Processing, Vol. 20, No. 9, 2012 , p.p. 2602 – 2612.

Hanhua Chen ; Hai Jin ; Xucheng Luo ; Yunhao Liu ; Tao Gu ; Chen, K. ; Ni, L.M., “BloomCast: Efficient and Effective Full-Text Retrieval in Unstructured P2P Networks”, IEEE Transactions on Parallel and Distributed Systems, Vol 23, No. 2, 2012 , p.p. 232 –241.

Sangwoo Moon ; Hairong Qi, “Hybrid Dimensionality Reduction Method Based on Support Vector Machine and Independent Component Analysis”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 23, No. 5, p.p. 749 – 761, 2012 .

Poonam Yadav, “Weighted Word Affinity Graph for Betterment of Spatial Information Descriptors”, Volume-02 , Issue-08, Page No :

-120, 2014 Poonam Yadav, “Weighted Word Affinity Graph for Betterment of Spatial Information Descriptors”, Volume-02 , Issue-08, Page No : 117-120, 2014.

Niknam, T. ; Azizipanah-Abarghooee, R. ; Roosta, A., “Reserve Constrained Dynamic Economic Dispatch: A New Fast SelfAdaptive Modified Firefly Algorithm”, IEEE Systems Journal, Vol. 6, No. 4, p.p. 635 – 646, 2012.

Poonam Yadav, „Dimensionality Reduction of Weighted Word Affinity Graph using Firefly Optimization”, International Journal of

Engineering Research & Technology, Vol. 3 - Issue 10, October – 2014. Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.

Downloads

Published

2024-02-26

How to Cite

Yadhav, P. (2024). Adaptive Firefly Optimization on Reducing High Dimensional Weighted Word Affinity Graph. COMPUSOFT: An International Journal of Advanced Computer Technology, 3(12), 1407–1411. Retrieved from https://ijact.in/index.php/j/article/view/243

Issue

Section

Original Research Article

Similar Articles

<< < 20 21 22 23 24 25 26 > >> 

You may also start an advanced similarity search for this article.