Abstract
Real-time environmental monitoring can provide vital situational awareness for effective management of natural resources. Effective operation of Shellfish farms depends on environmental conditions. In this paper we propose a supervised learning approach to predict the farm closures. This is a binary classification problem where farm closure is a function of environmental variables. A problem with this classification approach is that farm closure events occur with small frequency leading to class imbalance problem. Straightforward learning techniques tend to favour the majority class; in this case continually predicting no event. We present a new ensemble class balancing algorithm based on random undersampling to resolve this problem. Experimental results show that the class balancing ensemble performs better than individual and other state of art ensemble classifiers. We have also obtained an understanding of the importance of relevant environmental variables for shellfish farm closure. We have utilized feature ranking algorithms in this regard.
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References
Muttil, N., Chau, K.: Machine-learning paradigms for selecting ecologically significant input variables. Journal Engineering Applications of Artificial Intelligence 20(6), 735–744 (2007)
Rahman, A., Verma, B.: Novel layered clustering-based approach for generating ensemble of classifiers. IEEE Transactions on Neural Networks 22(5), 781–792 (2011)
Breiman, L.: Bagging predictors. Machine Learning 24(2), 123–140 (1996)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Breiman, L.: Pasting small votes for classification in large databases and on-line. Machine Learning 36, 85–103 (1999)
Martinez-Munoz, G., Hernandez-Lobato, D., Suarez, A.: An analysis of ensemble pruning techniques based on ordered aggregation. IEEE Trans. on Pattern Analysis and Machine Intelligence 31(2), 245–259 (2009)
Chen, L., Kamel, M.: A generalized adaptive ensemble generation and aggregation approach for multiple classifiers systems. Pattern Recognition 42, 629–644 (2009)
Nanni, L., Lumini, A.: Fuzzy bagging: a novel ensemble of classifiers. Pattern Recognition 39, 488–490 (2006)
Eschrich, S., Hall, L.O.: Soft partitions lead to better learned ensembles, pp. 406–411 (2002)
Schapire, R.: The strength of weak learnability. Machine Learning 5(2), 197–227 (1990)
Freund, Y., Schapire, R.: Decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55(1), 119–139 (1997)
Drucker, H., Cortes, C., Jackel, L., LeCun, Y., Vapnik, V.: Boosting and other ensemble methods. Neural Computation 6(6), 1289–1301 (1994)
Garcia-Pedrajas, N.: Constructing ensembles of classifiers by means of weighted instance selection. IEEE Trans. on Neural Networks 20(2), 258–277 (2009)
Chawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.: SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research 16, 341–378 (2002)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The weka data mining software: An update. SIGKDD Explorations 11(1) (2009)
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D’Este, C., Rahman, A., Turnbull, A. (2012). Predicting Shellfish Farm Closures with Class Balancing Methods. In: Thielscher, M., Zhang, D. (eds) AI 2012: Advances in Artificial Intelligence. AI 2012. Lecture Notes in Computer Science(), vol 7691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35101-3_4
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DOI: https://doi.org/10.1007/978-3-642-35101-3_4
Publisher Name: Springer, Berlin, Heidelberg
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