A new method for fraud detection in credit cards based on transaction dynamics in subspaces

[EN] This paper presents a new method for fraud detection in credit cards based on exploiting the dynamics of the card transactions. We hypothesize different behavior models in the use of the card between legitimate clients and fraudsters that are registered in the sequential pattern that follows the transactions. The method considers analyses in subspaces defined by two or three variables recorded in the transactions. From these subspaces, several dynamic features, such as transaction velocity and acceleration, are estimated as input vectors for a classification process. Linear and quadratic discriminant analysis and random forest are implemented as single classifiers. All the single classification results obtained for each of the subspaces are late fused to obtain an overall result using alpha integration algorithm. The proposed method was evaluated using a subset of real data with a very low fraud to legitimate transaction ratio. We demonstrated that the temporal dependence of card transactions exploited in different subspaces and fused to give an overall result improves the detection accuracy of fraud detection in credit cards.This work was supported by Generalitat Valenciana under grant PROMETEO/2019/109, and Spanish Administration and European Union grant TEC2017-84743-P.Salazar Afanador, A.; Safont, G.; Vergara Domínguez, L. (2019). A new method for fraud detection in credit cards based on transaction dynamics in subspaces. IEEE. 722-725. https://doi.org/10.1109/CSCI49370.2019.00137S72272

New applications of late fusion methods for EEG signal processing

[EN] Decision fusion consists in the combination of the outputs of multiple classifiers into a common decision that is more precise or stable. In most cases, however, only classical fusion techniques are considered. This work compares the performance of several state-of-the-art fusion methods on new applications of automatic stage classification of several neuropsychological tests. The tests were staged into three classes: stimulus display, retention interval, and subject response. The considered late fusion methods were: alpha integration; copulas; Dempster-Shafer combination; independent component analysis mixture models; and behavior knowledge space. Late fusion was able to improve the performance for the task, with alpha integration yielding the most stable result.This work was supported by Generalitat Valenciana under grant PROMETEO/2019/109 and Spanish Administration and European Union grant TEC2017-84743-P.Safont, G.; Salazar Afanador, A.; Vergara Domínguez, L. (2019). New applications of late fusion methods for EEG signal processing. IEEE. 617-621. https://doi.org/10.1109/CSCI49370.2019.00116S61762

A proxy learning curve for the Bayes classifier

[EN] In this paper, a theoretical learning curve is derived for the multi-class Bayes classifier. This curve fits general multivariate parametric models of the class-conditional probability density. The derivation uses a proxy approach based on analyzing the convergence of a statistic which is proportional to the posterior probability of the true class. By doing so, the curve depends only on the training set size and on the dimension of the feature vector; it does not depend on the model parameters. Essentially, the learning curve provides an estimate of the reduction in the excess of the probability of error that can be obtained by increasing the training set size. This makes it attractive in order to deal with the practical problems of defining appropriate training set sizes.Acknowledgments Grant TEC2017-84743-P funded by MCIN/AEI/10.13039/50110 0 011033 and by the European Union.Salazar Afanador, A.; Vergara Domínguez, L.; Vidal, E. (2023). A proxy learning curve for the Bayes classifier. Pattern Recognition. 136:1-14. https://doi.org/10.1016/j.patcog.2022.10924011413

Application of independent component analysis for evaluation of ashlar masonry walls

[EN] This paper presents a novel application of Independent Component Analysis (ICA) to the evaluation of ashlar masonry walls inspected with Ground Penetrating Radar (GPR). ICA is used as preprocessor to eliminate the background from the backscattered signals. Thus, signal-to-noise ratio of the GPR signals is enhanced. Several experiments were made on scale models of historic ashlar masonry walls. These models were loaded with different weights, and the corresponding B-Scans were obtained. ICA shows the best performance to enhance the quality of the B-Scans compared with classical methods used in GPR signal processing.This work has been supported by the Generalitat Valenciana under grant PROMETEO/2010/040, and the Spanish Administration and the FEDER Programme of the European Union under grant TEC 2008-02975/TEC.Salazar Afanador, A.; Safont Armero, G.; Vergara Domínguez, L. (2011). Application of independent component analysis for evaluation of ashlar masonry walls. Lecture Notes in Computer Science. 6691(1):469-476. https://doi.org/10.1007/978-3-642-21498-1_59S46947666911Salazar, A., Unió, J.M., Serrano, A., Gosalbez, J.: Neural networks for defect detection in non-destructive evaluation by sonic signals. In: Sandoval, F., Prieto, A.G., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 638–645. Springer, Heidelberg (2007)Salazar, A., Vergara, L., Llinares, R.: Learning material defect patterns by separating mixtures of independent component analyzers from NDT sonic signals. Mechanical Systems and Signal processing 24(6), 1870–1886 (2010)Zhao, A., Jiang, Y., Wang, W.: Exploring Independent Component Analysis for GPR Signal Processing. In: Progress In Electromagnetics Research Symposium 2005, pp. 750–753. The Electromagnetics Academy, Cambridge (2005)Abujarad, F., Omar, A.: Comparison of Independent-Component Analysis (ICA) Algorithms for GPR Detection of Non-Metallic Land Mines. In: Bruzzone, L. (ed.) Proceedings of SPIE Image and Signal Processing for Remote Sensing XII, vol. 6365, pp. 636516.1–636516.12. SPIE, Bellingham (2006)Liu, J.X., Zhang, B., Wu, R.B.: GPR Ground Bounce Removal Methods Based on Blind Source Separation. In: Progress In Electromagnetics Research Symposium 2006, pp. 256–259. The Electromagnetics Academy, Cambridge (2006)Verma, P.K., Gaikwad, A.N., Sigh, D., Nigam, M.J.: Analysis of Clutter Reduction Techniques for Through Wall Imaging in UWB Range. In: Progres. Electromagnetics Research B 2009, vol. 17, pp. 29–48. The Electromagnetics Academy, Cambridge (2009)Salazar, A., Vergara, L., Serrano, A., Igual, J.: A General Procedure for Learning Mixtures of Independent Component Analyzers. Pattern Recognition 43(1), 69–85 (2010)Cardoso, J.F., Souloumiac, A.: Blind beamforming for non Gaussian signals. IEE Proceedings-F 140(6), 362–370 (1993)Ziehe, A., Muller, K.R.: TDSEP - An Efficient Algorithm for Blind Separation Using Time Structure. In: Proceedings of the Eighth International Conference on Artificial Neural Networks ICANN 1998, Perspectives in Neural Computing, pp. 675–680 (1998)Reynolds, J.M.: An Introduction to Applied and Environmental Geophysics. Wiley, Chichester (1997)Igual, J., Camacho, A., Vergara, L.: A blind source separation technique for extracting sinusoidal interferences in ultrasonic non-destructive testing. Journal of VLSI Signal Processing 38, 25–34 (2004)Salazar, A., Gosálbez, J., Igual, J., Llinares, R., Vergara, L.: Two applications of independent component analysis for non-destructive evaluation by ultrasounds. In: Rosca, J.P., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds.) ICA 2006. LNCS, vol. 3889, pp. 406–413. Springer, Heidelberg (2006)Raghavan, R.S.: A Model for Spatially Correlated Radar Clutter. IEEE Trans. on Aerospace and Electronic Systems 27, 268–275 (1991)Salazar, A., Vergara, L.: ICA mixtures applied to ultrasonic nondestructive classification of archaeological ceramics. EURASIP Journal on Advances in Signal Processing, Article ID 125201, 11 (2010), doi:10.1155/2010/125201Salazar, A., Vergara, L., Miralles, R.: On including sequential dependence in ICA mixture models. Signal Processing 90(7), 2314–2318 (2010

Multiclass Alpha Integration of Scores from Multiple Classifiers

[EN] Alpha integration methods have been used for integrating stochastic models and fusion in the context of detection (binary classification). Our work proposes separated score integration (SSI), a new method based on alpha integration to perform soft fusion of scores in multiclass classification problems, one of the most common problems in automatic classification. Theoretical derivation is presented to optimize the parameters of this method to achieve the least mean squared error (LMSE) or the mínimum probability of error (MPE). The proposed alpha integrationmethod was tested on several sets of simulated and real data. The first set of experiments used synthetic data to replicate a problem of automatic detection and classification of three types of ultrasonic pulses buried in noise (four-class classification). The second set of experiments analyzed two databases (one publicly available and one private) of real polysomnographic records from subjects with sleep disorders. These records were automatically staged in wake, rapid eye movement (REM) sleep, and non-REM sleep (three-class classification). Finally, the third set of experiments was performed on a publicly available database of single-channel real electroencephalographic data that included epileptic patients and healthy controls in five conditions (five-class classification). In all cases, alpha integration performed better than the considered single classifiers and classical fusion techniques.This work was supported by the Spanish Administration and European Union under grant TEC2017-84743-P and Generalitat Valenciana under grant PROMETEO II/2014/032.Safont Armero, G.; Salazar Afanador, A.; Vergara Domínguez, L. (2019). Multiclass Alpha Integration of Scores from Multiple Classifiers. Neural Computation. 31(4):806-825. https://doi.org/10.1162/neco_a_01169S80682531

Nonlinear prediction based on independent component analysis mixture modelling

[EN] This paper presents a new algorithm for nonlinear prediction based on independent component analysis mixture modelling (ICAMM). The data are considered from several mutually-exclusive classes which are generated by different ICA models. This strategy allows linear local projections that can be adapted to partial segments of a data set while maintaining generalization (capability for nonlinear modelling) given the mixture of several ICAs. The resulting algorithm is a general purpose technique that could be applied to time series prediction, to recover missing data in images, etc. The performance of the proposed method is demonstrated by simulations in comparison with several classical linear and nonlinear methods. © 2011 Springer-Verlag.This work has been supported by the Generalitat Valenciana under grant PROMETEO/2010/040, and the Spanish Administration and the FEDER Programme of the European Union under grant TEC 2008-02975/TEC.Safont Armero, G.; Salazar Afanador, A.; Vergara Domínguez, L. (2011). Nonlinear prediction based on independent component analysis mixture modelling. Lecture Notes in Computer Science. 6691(1):508-515. https://doi.org/10.1007/978-3-642-21498-1_64S50851566911Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley & Sons, New York (2001)Lee, T.W., Lewicki, M.S., Sejnowski, T.J.: ICA mixture models for unsupervised classification of non-gaussian classes and automatic context switching in blind signal separation. IEEE Trans. on Patt. Analysis and Mach. Intellig. 22(10), 1078–1089 (2000)Malaroiu, S., Kiviluoto, K., Oja, E.: ICA Preprocessing for Time Series Prediction. In: 2nd International Workshop on ICA and BSS (ICA 2000), pp. 453–457 (2000)Pajunen, P.: Extensions of Linear Independent Component Analysis: Neural and Information-Theoretic Methods. Ph.D. Thesis, Helsinki University of Technology (1998)Gorriz, J.M., Puntonet, C.G., Salmeron, G., Lang, E.W.: Time Series Prediction using ICA Algorithms. In: Proceedings of the 2nd IEEE International Workshop on Intelligent Data Acquisit. and Advanc. Comput. Systems: Technology and Applications, pp. 226–230 (2003)Wang, C.Z., Tan, X.F., Chen, Y.W., Han, X.H., Ito, M., Nishikawa, I.: Independent component analysis-based prediction of O-Linked glycosylation sites in protein using multi-layered neural networks. In: IEEE 10th Internat. Conf. on Signal Processing, pp. 1–4 (2010)Zhang, Y., Teng, Y., Zhang, Y.: Complex process quality prediction using modified kernel partial least squares. Chemical Engineering Science 65, 2153–2158 (2010)Salazar, A., Vergara, L., Serrano, A., Igual, J.: A general procedure for learning mixtures of independent component analyzers. Pattern Recognition 43(1), 69–85 (2010)Bersektas, D.: Nonlinear programming. Athena Scientific, Massachusetts (1999)Cardoso, J.F., Souloumiac, A.: Blind beamforming for non gaussian signals. IEE Proceedings-F 140(6), 362–370 (1993)Salazar, A., Vergara, L., Llinares, R.: Learning material defect patterns by separating mixtures of independent component analyzers from NDT sonic signals. Mechanical Systems and Signal processing 24(6), 1870–1886 (2010)Salazar, A., Vergara, L.: ICA mixtures applied to ultrasonic nondestructive classification of archaeological ceramics. EURASIP Journal on Advances in Signal Processing, vol. 2010, p.11, Article ID 12520111 (2010), doi:10.1155/2010/125201Salazar, A., Vergara, L., Miralles, R.: On including sequential dependence in ICA mixture models. Signal Processing 90(7), 2314–2318 (2010)Raghavan, R.S.: A Model for Spatially Correlated Radar Clutter. IEEE Trans. on Aerospace and Electronic Systems 27, 268–275 (1991

On Training Road Surface Classifiers by Data Augmentation

[EN] It is demonstrated that data augmentation is a promising approach to reduce the size of the captured dataset required for training automatic road surface classifiers. The context is on-board systems for autonomous or semi-autonomous driving assistance: automatic power-assisted steering. Evidence is obtained by extensive experiments involving multiple captures from a 10-channel multisensor deployment: three channels from the accelerometer (acceleration in the X, Y, and Z axes); three microphone channels; two speed channels; and the torque and position of the handwheel. These captures were made under different settings: three worm-gear interface configurations; hands on or off the wheel; vehicle speed (constant speed of 10, 15,20, 30 km/h, or accelerating from 0 to 30 km/h); and road surface (smooth flat asphalt, stripes, or cobblestones). It has been demonstrated in the experiments that data augmentation allows a reduction by an approximate factor of 1.5 in the size of the captured training dataset.This research was funded by MCIN/AEI/10.13039/501100011033 and by the European Union, grant number TEC2017-84743-P.Salazar Afanador, A.; Rodríguez, A.; Vargas, N.; Vergara Domínguez, L. (2022). On Training Road Surface Classifiers by Data Augmentation. Applied Sciences. 12(7):1-11. https://doi.org/10.3390/app1207342311112

Estimating the Laplacian matrix of Gaussian mixtures for signal processing on graphs

[EN] Recent works in signal processing on graphs have been driven to estimate the precision matrix and to use it as the graph Laplacian matrix. The normalized elements of the precision matrix are the partial correlation coefficients which measure the pairwise conditional linear dependencies of the graph. However, the non-linear dependencies inherent in any non-Gaussian model cannot be captured. We propose in this paper a generalized partial correlation coefficient which is derived by assuming an underlying multivariate Gaussian Mixture Model of the observations. Exact and approximate methods are proposed to estimate the generalized partial correlation coefficients from estimates of the Gaussian Mixture Model parameters. Thus it may find application in any non-Gaussian scenario where the Laplacian matrix is to be learned from training signals. (C) 2018 Elsevier B.V. All rights reserved.This work was supported by Spanish Administration (Ministerio de Economia y Competitividad) and European Union (FEDER) under grant TEC2014-58438-R, and Generalitat Valenciana under grant PROMETEO II/2014/032.Belda, J.; Vergara Domínguez, L.; Salazar Afanador, A.; Safont Armero, G. (2018). Estimating the Laplacian matrix of Gaussian mixtures for signal processing on graphs. Signal Processing. 148:241-249. https://doi.org/10.1016/j.sigpro.2018.02.017S24124914

Fusion Methods for Biosignal Analysis: Theory and Applications

Salazar Afanador, A.; Zarzoso, V.; Rosa-Zurera, M.; Vergara Domínguez, L. (2017). Fusion Methods for Biosignal Analysis: Theory and Applications. Computational Intelligence and Neuroscience. (1):1-2. doi:10.1155/2017/7152546S12

Experimental Study of Hierarchical Clustering for Unmixing of Hyperspectral Images

[EN] Estimation of the number of materials that are present in a hyperspectral image is a necessary step in many hyperspectral image processing algorithms, including classification and unmixing. Previously, we presented an algorithm that estimated the number of materials in the image using clustering principles. This algorithm is an iterative approach with two input parameters: the initial number of materials (P0) and the number of materials added in each iteration (¿). Since the choice of P0 and ¿ can have a large impact on the estimation accuracy. In this paper, we made an experimental study of the effect of these parameters on the algorithm performance. Thus, we show that the choice of a large ¿ can significantly reduce the estimation accuracy. These results can help to make an appropriate choice of these two parameters.This research has been supported by Generalitat Valenciana, grant PROMETEO 2019/109.Prades Nebot, J.; Salazar Afanador, A.; Safont, G.; Vergara Domínguez, L. (2021). Experimental Study of Hierarchical Clustering for Unmixing of Hyperspectral Images. IEEE. 1-5. https://doi.org/10.1109/ICARES53960.2021.96652011