Yager–Rybalov Triple Π Operator as a Means of Reducing the Number of Generated Clusters in Unsupervised Anuran Vocalization Recognition
The Learning Algorithm for Multivariate Data Analysis (LAMDA) is an unsupervised fuzzy-based classification methodology. The operating principle of LAMDA is based on finding the datum-cluster relationship obtained by means of the Global Adequacy Degrees (GADs) of the Marginal Adequacy Degrees (MADs) of all the data attributes. In comparison with other unsupervised clustering algorithms, LAMDA does not require the number of classes as input parameter; however, in some applications, the quantity of obtained clusters does not correspond with the number of desired classes. Typically, this issue is overcome by merging interrelated clusters within the same class; nevertheless, in some applications the number of generated clusters related to the same class reaches a non-desired and impractical number. In LAMDA, the number of generated clusters is controlled by using a linear mixed connective with an exigency index α. This connective is an unnatural aggregation operator of the MADs, which adds an additional parameter to set up. In this paper, a full reinforcement operator (Yager–Rybalov Triple Π) is used as aggregation operator for merging the information contained in the MADs. This approach significantly reduces the number of generated classes and suppresses the LAMDA dependence of the parameter α. The proposed approach was tested in a case study related to unsupervised anuran vocalization recognition. A database of advertisement calls of six anuran (frog) species for testing this proposal was selected. All 102 vocalizations were correctly identified (100% of accuracy) and solely the desired classes were generated by the algorithm (establishing a cluster-class bijection).
KeywordsFuzzy clustering Fuzzy connective Bioacoustics Anuran Aggregation operator Bipolar scale
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- 1.Aguilar-Martin, J., López de Mantarás, R.: The process of classification and learning the meaning of linguistic descriptors or concepts. Approximate Reasoning in Decision Analysis, 165–175 (1982)Google Scholar
- 2.Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. John Wiley & Sons, New York (2001); J. Classif. 24(2) (September 2007)Google Scholar
- 3.Tzanakou, E.M.: Supervised and Unsupervised Pattern Recognition: Feature Extraction and Computational Intelligence. CRC Press, New York (2000)Google Scholar
- 5.Proceedings of the 4th International Workshop on Detection, Classification and Localization of Marine Mammals Using Passive Acoustics and 1st International Workshop on Density Estimation of Marine Mammals Using Passive Acoustics. Applied Acoustics 71(11), 991–1112 (November 2010)Google Scholar
- 6.Sánchez, M., Prats, F., Agell, N., Aguilar-Martin, J.: A Characterization of Linearly Compensated Hybrid Connectives Used in Fuzzy Classifications. In: ECAI, pp. 1081–1082. IOS Press (2004)Google Scholar
- 9.Bedoya, C., Uribe, C., Isaza, C.: Unsupervised Feature Selection Based on Fuzzy Clustering for Fault Detection of the Tennessee Eastman Process. In: Proceedings of the 13th Ibero-American Conference on Artificial Intelligence (IBERAMIA), Cartagena de Indias, Colombia, pp. 350–360 (2012)Google Scholar
- 13.Isaza, C.: Diagnostic par Techniques d‘apprentissage Floues: Conception d‘une Methode de Validation et d‘optimisation des Partitions. PhD thesis, Laboratoire d’Analyse et d’Architecture des Syst‘emes du CNRS (October 2007)Google Scholar
- 14.Emilion, R., Regis, S., Doncescu, A.: A General Version of the Triple Pi Operator. International Journal of Iintelligent Systems, 1–18 (2013)Google Scholar
- 15.Ibañez, R., Stanley, A., Ryan, M., Jaramillo, C.: Vocalizaciones de ranas y sapos del Monumento Natural Barro Colorado. Parque Nacional Soberanía y áreas adyacentes. Sony MusicEntertaiment (Central America) S.A. (1999)Google Scholar
- 16.Selesnick, I.W., Burrus, C.S.: Generalized Digital Butterworth Filter Design. In: Proceedings of the IEEE Int. Conf. Acoust., Speech, Signal Processing, vol. 3 (May 1996)Google Scholar
- 17.Zhao, X., O’Shaughnessy, D.: A new hybrid approach for automatic speech signal segmentation using silence signal detection, energy convex hull, and spectral variation. In: Canadian Conference on Electrical and Computer Engineering, CCECE 2008, pp. 4–7 (May 2008)Google Scholar
- 18.Mermelstein, P.: Distance measures for speech recognition, psychological and instrumental. Pattern Recognition and Artificial Intelligence, 374–388 (1976)Google Scholar