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Omkar, S.N.
- Application of Ant-miner Algorithm to Extract Knowledge from Star Excursion Balance Test
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Authors
Affiliations
1 Indian Institute of Science, Bangalore, IN
2 Indian Institute of Technology(Madras), Chennai, IN
1 Indian Institute of Science, Bangalore, IN
2 Indian Institute of Technology(Madras), Chennai, IN
Source
Indian Journal of Physiotherapy & Occupational Therapy-An International Journal, Vol 3, No 3 (2009), Pagination: 31-36Abstract
Star Excursion balance test (SEBT) is a functional test to assess the dynamic balance and lower body stability. The knee condition plays a significant role on stability, and hence on the results. The SEBT results being high dimensional, gathering information about the knee condition from it becomes very difficult for doctors and physiotherapists. Knowledge extracted from the data will assist the doctors to analyze the SEBT results and diagnose the patients better. In this work, Ant colony based algorithm, Ant-Miner is used to extract knowledge from the data. Rules for classification of the data are obtained and the merit of Ant- Miner is highlighted by the simplicity of the rules.Keywords
Sebt, Knowledge Extraction, Ant-colony Optimization, Ant-miner, Data Cluster
References
- J. Hertel, Braham RA, Hale SA, Olmsted-Kramer LC. Simplifying the star excursion balance test: analyses of subjects with and without chronic ankle instability. J Orthop Sports Phys Ther. 2006 Mar;36(3):131-7.
- L.C. Olmsted, Carcia CR, Hertel J, Shultz SJ. Efficacy of the star excursion balance tests in detecting reach deficits in subjects with chronic ankle instability. J Athl Train. 2002;37:501–506.
- A. Phillip, Gribble and Jay Hertel, Department of Kinesiology Athletic Training Research Lab Pennsylvania State University, Considerations for normalizing Measures of the Star Excursion Balance Test, asurement in Physical Education and Exercise Science, 7(2), 89–100.
- An-Pin Chen, Mu-Yen Chen, Integrating extended classifier system and knowledge extraction model for financial investment prediction: An empirical study, Expert Systems with Applications 31 (2006) 174–183.
- F M Facca, Pier Luca Lanzi, Mining interesting knowledge from weblogs: a survey, Data & Knowledge Engineering 53 (2005) 225–241.
- J.F. Remma, F. Alexandrea, Knowledge extraction using artificial neural networks: application to radar target identification, Signal Processing 82 (2002) 117–120.
- G. Castellano, C. Castiello, A.M. Fanelli, C. Mencar, Knowledge discovery by a neuro-fuzzy modeling framework, Fuzzy Sets and Systems 149 (2005) 187–207.
- Fernando Gomez, Carlos Segami, Semantic interpretation and knowledge extraction, Knowledge-Based Systems (2006). In Press.
- W³odzis³aw Duch, Rafa³ Adamczak, Krzysztof Gra¸bczewski and Norbert Jankowski, Neural methods of knowledge extraction, Control and Cybernetics, vol.29 (2000) No. 4.
- Greer B. Kingston, Holger R. Maier, Martin F. Lambert, A probabilistic method for assisting knowledge extraction fromartificial neural networks used for hydrological prediction, Mathematical and Computer Modelling 44 (2006) 499–512.
- Ta-Cheng Chen, Tung-Chou Hsu, A GA based approach for mining breast cancer pattern, Expert Systems with Applications 30 (2006) 674–681.
- M. Dorigo, Optimization, Learning and Natural Algorithms, Ph.D. Thesis, Politecnico di Milano, Italy, 1992.
- C Blum, Ant colony optimization: Introduction and recent trends, Physics of Life Reviews 2 (2005) 353–373.
- Marco Dorigo, Christian Blumb, Ant colony optimization theory: A survey, Theoretical Computer Science 344 (2005) 243 – 278.
- M. Dorigo, V. Maniezzo, A. Colorni, The ant system: optimization by a colony of cooperating agents, IEEE Transactions on Systems, Mans, and Cybernetics 1 (26) (1996).
- L. Gambardella, E. Taillard, M. Dorigo, Ant colonies for the QAP, Technical Report 97-4, IDSIA, Lugano, Switzerland, 1997.
- A. Colorni, M. Dorigo, V. Maniezzo, M. Trubian, Ant system for job-shop scheduling, JORBEL-Belgian Journal of Operations Research Statistics and Computer Science 34 (1) (1994) 39–53.
- B. Bullnheimer, R.F. Hartl, C. Strauss, Applying the ant system to the vehicle routing problem, in: Second Metaheuristics International Conference, MIC’97, Sophia-Antipolis, France, 1997.
- R. Schoonderwoerd, O. Holland, J. Bruten, L. Rothkrantz, Ant-based load balancing in telecommunications networks, Adaptive Behavior 5 (2) (1997) 169–207
- P. S. Shelokar, V. K. Jayaraman, B. D. Kulkarni, An ant colony classifier system: application to some process engineering problems, Computers and Chemical Engineering 28 (2004) 1577–1584.
- Parpinelli, R.S., Lopes, H.S., and Freitas, 2002. Data mining with an ant colony optimization algorithm. IEEE Trans. Evol. Comput. 6, 321-332.
- J. Hertel, Miller SJ, Denegar CR. Intratester and intertester reliability during the Star Excursion Balance Test. J Sport Rehabil. 2000;9:104–116.