Model for Evaluating the Effectiveness of Search Operations

Kulik Sergey Dmitrievich

Abstract


An automated search with human involvement consisting of two stages is given detailed consideration in this paper. In the first stage, a search without direct human involvement is implemented. In the second stage, the search assumes human involvement. Evaluations of the search operations’ effectiveness are presented. These operations are implemented for searching one object exclusively among a variety of similar objects. The average number of similar objects recommended for further analysis was used as effectiveness indicator. A set of numerical evaluation criteria for search effectiveness is introduced. The basis of the search block is a pattern recognition algorithm characterized by two probabilities: 1) probability of missing a target, and 2) false alarm probability. An analytical model of the search block was developed. In this paper particular attention is given to the average length of the recommendatory list as an effectiveness indicator. Four properties of this indicator were determined.


Full Text:

PDF

References


Taha, H.A., Operations Research: An Introduction (8th Edition). Upper Saddle River, New Jersey 07458, Pearson Prentice Hall, 2007, p. 813.

Nakamura, K. & Iwai, S., Topological Fuzzy Sets as a Quantitative Description of Analogical Inference and Its Application to Question-Answering Systems for Information Retrieval, IEEE Transactions on Systems, Man and Cybernetics, 12(2), pp. 193-204, March 1982.

Wald, A., Sequential Analysis (Second Printing, November, 1948). Mineola, N.Y., Dover Publications, p. 212, 2004.

Kulik, S.D., Algorithms for Pattern Recognition and Simulation of Automated Factographic Information Retrieval Systems, Neuro-computers: Design and Application, 9-10, pp. 115-127, 2002.

Ko, J., Si, L., Nyberg, E. & Mitamura, T., Probabilistic Models for Answer-Ranking in Multilingual Question-Answering, ACM Transactions on Information Systems (TOIS), 28(3), Article 16, p. 37, 2010.

Mateescu G., Sosonkina M. & Thompson P., A New Model for Probabilistic Information Retrieval on the Web, Workshop on Web Analytics, J. Ghosh & J. Srivastava, eds., Arlington, Second SIAM International Conference on Data Mining, VA, pp. 17-27, 2002.

Glitho R.H., Olougouna E. & Pierre S., Mobile Agents and Their Use for Information Retrieval: A Brief Overview and an Elaborate Case Study, Network IEEE, 16(1), pp. 34-41, 2002.

Fukunaga, K., Introduction to Statistical Pattern Recognition (2nd edition), Elsevier Academic Press, San Diego, San Francisco, New York, Boston, London, Sydney, Tokyo, p. 592, 1990.

Huang H. & He H., Super-Resolution Method for Face Recognition Using Nonlinear Mappings on Coherent Features, IEEE Transactions on Neural Networks, 22(1), pp. 121-130, 2011.

Galushkin, A.I., Neural Networks Theory, Springer Berlin Heidelberg New York, p. 396, 2007.

Haykin, S., Neural Networks–A Comprehensive Foundation, Second Edition, Pearson Education, Inc., 1999 (reprint), 2005.

Kruglov I.A., Mishulina, O.A. & Bakirov, M.B., Quantile-based Decision-Making Rule of the Neural Networks Committee for Ill-Posed Approximation Problems, Neurocomputing, 96, pp. 74-82, 2012.

Vasconcelos, N., Minimum Probability of Error Image Retrieval, IEEE Transactions on Signal Processing, 52(8), pp. 2322-2336, August, 2004.

Gui, C., Liu, J., Xu, C. & Lu, H., Web Image Retrieval via Learning Semantics of Query Image, ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo (June 28 2009-July 3 2009), IEEE Press Piscataway, NJ, USA, pp. 1476-1479, 2009.

Costa Pereira, J., Coviello, E., Doyle, G., Rasiwasia, N., Lanckriet, G., Levy, R. & Vasconcelos, N., On the Role of Correlation and Abstraction in Cross-Modal Multimedia Retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(3), pp. 521-535, 2014.

Blei, D., Probabilistic Topic Models, Communications of the ACM, 55, (4), pp. 77-84, 2012.

Miotto, R. & Orio N., A Probabilistic Model to Combine Tags and Acoustic Similarity for Music Retrieval, ACM Transactions on Information Systems (TOIS), 30(2), Article 8, p. 29, 2012.

Stathopoulos, V. & Jose, J.M., Bayesian Probabilistic Models for Image Retrieval, Journal of Machine Learning Research (JMLR): Workshop and Conference Proceedings, 17, from 2nd Workshop on Applications of Pattern Analysis (WAPA), 19-21 October, 2011, CIEM, Castro Urdiales, Spain, pp. 41-47, 2011.

Losee, R.M. & Church Jr., L., Information Retrieval with Distributed Databases: Analytic Models of Performance, IEEE Transactions on parallel and distributed systems, 15(1), pp. 8-27, 2004.

Feller, W. An Introduction to Probability Theory and its Applications, Vol.1, 3rd Ed., John Wiley & Sons. New York, p. 509, 1968.

Imre S. & Balazs, F., Quantum Computing and Communications: An Engineering Approach, Chichester, West Sussex, England: John Wiley & Sons, p. 283, 2005.




DOI: http://dx.doi.org/10.5614%2Fitbj.ict.res.appl.2015.9.2.5

Refbacks

  • There are currently no refbacks.


Contact Information:

ITB Journal Publisher, LPPM – ITB, 

Center for Research and Community Services (CRCS) Building Floor 7th, 
Jl. Ganesha No. 10 Bandung 40132, Indonesia,

Tel. +62-22-86010080,

Fax.: +62-22-86010051;

e-mail: jictra@lppm.itb.ac.id.