Predicting user behavior using data profiling and hidden Markov model

Mental health disorders affect many aspects of patient’s lives, including emotions, cognition, and especially behaviors. E-health technology helps to collect information wealth in a non-invasive manner, which represents a promising opportunity to construct health behavior markers. Combining such user behavior data can provide a more comprehensive and contextual view than questionnaire data. Due to behavioral data, we can train machine learning models to understand the data pattern and also use prediction algorithms to know the next state of a person’s behavior. The remaining challenges for this issue are how to apply mathematical formulations to textual datasets and find metadata that aids to identify the person’s life pattern and also predict the next state of his comportment. The main idea of this work is to use a hidden Markov model (HMM) to predict user behavior from social media applications by analyzing and detecting states and symbols from the user behavior dataset. To achieve this goal, we need to analyze and detect the states and symbols from the user behavior dataset, then convert the textual data to mathematical and numerical matrices. Finally, apply the HMM model to predict the hidden user behavior states. We tested our program and identified that the log-likelihood was higher and better when the model fits the data. In any case, the results of the study indicated that the program was suitable for the purpose and yielded valuable data

Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes

Modelling semantic context for novelty detection in wildlife scenes

Novelty detection is an important functionality that has found many applications in information retrieval and processing. In this paper we propose a novel framework that deals with novelty detection for multiple-scene image sets. Working with wildlife image data, the framework starts with image segmentation, followed by feature extraction and classification of the image blocks extracted from image segments. The labelled image blocks are then scanned through to generate a co-occurrence matrix of object labels, representing the semantic context within the scene. The semantic co-occurrence matrices then undergo binarization and principal component analysis for dimension reduction, forming the basis for constructing one-class models for each scene category. An algorithm for outlier detection that employs multiple one-class models is proposed. An advantage of our approach is that it can be used for scene classification and novelty detection at the same time. Our experiments show that the proposed approach algorithm gives favourable performance for the task of detecting novel wildlife scenes, and binarization of the label co-occurrence matrices helps to significantly increase the robustness in dealing with the variation of scene statistics.UnpublishedD.E. Berlyne. Stimulus Selection and Conflict. McGraw-Hill Book Company, 1960. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993–1022, 2003. A. Bosch, A. Zisserman, and X. Munoz. Scene classification via pLSA. In Proceedings of the European Conference on Computer Vision, pages 517–530, 2006. V. Chandola, A. Banerjee, and V. Kumar. Anomaly detection: A survey. ACM Computing Surveys, 41:1–58, 2009. Christine Connolly. Wildlife-spotting robots. Sensor Reviews, 27:282–287, 2007. Jia Deng, Wei Dong, R. Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 248–255, Los Alamitos, CA, USA, 2009. IEEE Computer Society. Y. Deng, , and B.S. Manjunath. Unsupervised segmentation of color-texture regions in images and video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(8):800–810, Aug 2001. Mark J. Fenske, Elissa Aminoff, Nurit Gronau, and Moshe Bar. Top-down facilitation of visual object recognition: object-based and context-based contributions. Progress in Brain Research, 155:3–21, 2006. R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman. Learning object categories from google’s image search. In International Conference on Computer Vision, volume 2, pages 1816–1823, 2005. L. Fei-Fei and P. Perona. A bayesian hierarchical model for learning natural scence categories. In IEEE Conference on Computer Vision and Pattern Recognition, pages 524–531, 2005. Evgeniy Gabrilovich, Susan Dumais, and Eric Horvitz. Newsjunkie: providing personalized newsfeeds via analysis of information novelty. In WWW’04: Proceedings of the 13th international conference on World Wide Web, pages 482–490, New York, NY, USA, 2004. ACM. K. Hempstalk, E. Frank, and I.H. Witten. One-class classification by combining density and class probability estimation. In Proc. ECML/PKDD’08, volume 5211 of Lecture Notes in Computer Science, pages 505–519, Berlin, September 2008. Springer. R. M. Haralick, K. Shanmugam, and I. Dinstein. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3:610–621, 1973. F. Jing, M. Li, L. Zhang, H. Zhang, and B. Zhang. Learning in region-based image retrieval. In International Conference on Image and Video Retrieval, Urbana-Champaign, Illinois, 2003. H. Katti, K. Y. Bin, T. S. Chua, and M. Kankanhalli. Pre-attentive discrimination of interestingness in images. In International Conference of Multimedia and Expo (ICME), Hannover, Germany, June 23-26, 2008. Shehzad Khalid. Motion-based behaviour learning, profiling and classification in the presence of anomalies. Pattern Recognition, 43(1):173 – 186, 2010. Xiaoyan Li and W. Bruce Croft. An information-pattern-based approach to novelty detection. Information Processing and Management, 44(3):1159 – 1188, 2008. L-J. Li, R. Socher, and L. Fei-Fei. Towards total scene understanding:classification, annotation and segmentation in an automatic framework. In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), pages 2036–2043, 2009. B. S. Manjunath, J. R. Ohm, V. V. Vasudevan, and A. Yamada. Color and texture descriptors. Circuits and Systems for Video Technology, IEEE Transactions on, 11(6):703–715, 2001. G. Manson, G. Pierce, and K. Worden. On the long-term stability of normal condition for damage detection in a composite panel. Key Engineering Materials, 204-205:359–370, 2001. Markos Markou and Sameer Singh. Novelty detection: a review—part 1: statistical approaches. Signal Process., 83(12):2481–2497, 2003. Yu-Fei Ma and Hong-Jiang Zhang. Contrast-based image attention analysis by using fuzzy growing. In Multimedia’03: Proceedings of the 11th ACM International Conference on Multimedia, pages 374–381, New York, NY, USA, 2003. ACM. Dragoljub Pokrajac, Aleksandar Lazarevic, and Longin Jan Latecki. Incremental local outlier detection for data streams. In Proceedings of IEEE Symposium on Computational Intelligence and Data Mining, pages 504–515, 2007. Animesh Patcha and Jung-Min Park. An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks, 51(12):3448 – 3470, 2007. Paul J. Silvia. Exploring the Psychology of Interest, volume 56. Oxford University Press, 2006. J. A. Stirk and G. Underwood. Low-level visual saliency does not predict change detection in natural scences. Journal of Vision, 7(10):3:1–10, 2007. J. Wang, J. Li, and G. Wiederhold. Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9):947–963, 2001

Modelling semantic context for novelty detection in wildlife scenes

Novelty detection is an important functionality that has found many applications in information retrieval and processing. In this paper we propose a novel framework that deals with novelty detection for multiple-scene image sets. Working with wildlife image data, the framework starts with image segmentation, followed by feature extraction and classification of the image blocks extracted from image segments. The labelled image blocks are then scanned through to generate a co-occurrence matrix of object labels, representing the semantic context within the scene. The semantic co-occurrence matrices then undergo binarization and principal component analysis for dimension reduction, forming the basis for constructing one-class models for each scene category. An algorithm for outlier detection that employs multiple one-class models is proposed. An advantage of our approach is that it can be used for scene classification and novelty detection at the same time. Our experiments show that the proposed approach algorithm gives favourable performance for the task of detecting novel wildlife scenes, and binarization of the label co-occurrence matrices helps to significantly increase the robustness in dealing with the variation of scene statistics.UnpublishedD.E. Berlyne. Stimulus Selection and Conflict. McGraw-Hill Book Company, 1960. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993–1022, 2003. A. Bosch, A. Zisserman, and X. Munoz. Scene classification via pLSA. In Proceedings of the European Conference on Computer Vision, pages 517–530, 2006. V. Chandola, A. Banerjee, and V. Kumar. Anomaly detection: A survey. ACM Computing Surveys, 41:1–58, 2009. Christine Connolly. Wildlife-spotting robots. Sensor Reviews, 27:282–287, 2007. Jia Deng, Wei Dong, R. Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. ImageNet: A large-scale hierarchical image database. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pages 248–255, Los Alamitos, CA, USA, 2009. IEEE Computer Society. Y. Deng, , and B.S. Manjunath. Unsupervised segmentation of color-texture regions in images and video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(8):800–810, Aug 2001. Mark J. Fenske, Elissa Aminoff, Nurit Gronau, and Moshe Bar. Top-down facilitation of visual object recognition: object-based and context-based contributions. Progress in Brain Research, 155:3–21, 2006. R. Fergus, L. Fei-Fei, P. Perona, and A. Zisserman. Learning object categories from google’s image search. In International Conference on Computer Vision, volume 2, pages 1816–1823, 2005. L. Fei-Fei and P. Perona. A bayesian hierarchical model for learning natural scence categories. In IEEE Conference on Computer Vision and Pattern Recognition, pages 524–531, 2005. Evgeniy Gabrilovich, Susan Dumais, and Eric Horvitz. Newsjunkie: providing personalized newsfeeds via analysis of information novelty. In WWW’04: Proceedings of the 13th international conference on World Wide Web, pages 482–490, New York, NY, USA, 2004. ACM. K. Hempstalk, E. Frank, and I.H. Witten. One-class classification by combining density and class probability estimation. In Proc. ECML/PKDD’08, volume 5211 of Lecture Notes in Computer Science, pages 505–519, Berlin, September 2008. Springer. R. M. Haralick, K. Shanmugam, and I. Dinstein. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3:610–621, 1973. F. Jing, M. Li, L. Zhang, H. Zhang, and B. Zhang. Learning in region-based image retrieval. In International Conference on Image and Video Retrieval, Urbana-Champaign, Illinois, 2003. H. Katti, K. Y. Bin, T. S. Chua, and M. Kankanhalli. Pre-attentive discrimination of interestingness in images. In International Conference of Multimedia and Expo (ICME), Hannover, Germany, June 23-26, 2008. Shehzad Khalid. Motion-based behaviour learning, profiling and classification in the presence of anomalies. Pattern Recognition, 43(1):173 – 186, 2010. Xiaoyan Li and W. Bruce Croft. An information-pattern-based approach to novelty detection. Information Processing and Management, 44(3):1159 – 1188, 2008. L-J. Li, R. Socher, and L. Fei-Fei. Towards total scene understanding:classification, annotation and segmentation in an automatic framework. In Proc. IEEE Computer Vision and Pattern Recognition (CVPR), pages 2036–2043, 2009. B. S. Manjunath, J. R. Ohm, V. V. Vasudevan, and A. Yamada. Color and texture descriptors. Circuits and Systems for Video Technology, IEEE Transactions on, 11(6):703–715, 2001. G. Manson, G. Pierce, and K. Worden. On the long-term stability of normal condition for damage detection in a composite panel. Key Engineering Materials, 204-205:359–370, 2001. Markos Markou and Sameer Singh. Novelty detection: a review—part 1: statistical approaches. Signal Process., 83(12):2481–2497, 2003. Yu-Fei Ma and Hong-Jiang Zhang. Contrast-based image attention analysis by using fuzzy growing. In Multimedia’03: Proceedings of the 11th ACM International Conference on Multimedia, pages 374–381, New York, NY, USA, 2003. ACM. Dragoljub Pokrajac, Aleksandar Lazarevic, and Longin Jan Latecki. Incremental local outlier detection for data streams. In Proceedings of IEEE Symposium on Computational Intelligence and Data Mining, pages 504–515, 2007. Animesh Patcha and Jung-Min Park. An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer Networks, 51(12):3448 – 3470, 2007. Paul J. Silvia. Exploring the Psychology of Interest, volume 56. Oxford University Press, 2006. J. A. Stirk and G. Underwood. Low-level visual saliency does not predict change detection in natural scences. Journal of Vision, 7(10):3:1–10, 2007. J. Wang, J. Li, and G. Wiederhold. Simplicity: semantics-sensitive integrated matching for picture libraries. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(9):947–963, 2001