In this paper, we propose a music mood classification system that reflects a user's profile based on a belief that music mood perception is subjective and can vary depending on the user's profile such as age or gender. To this end, we first define a set of generic mood descriptors. Secondly, we make up several user profiles according to the age and gender. We then obtain musical items, for each group, to separately train the statistical models. Using the two different user models, we verify our hypothesis that the user profiles play an important role in mood perception by showing that both models achieve higher classification accuracy when the test data and the mood model are of the same kind. Applying our system to automatic play list generation, we also demonstrate that considering the difference between the user groups in mood perception has a significant effect in computing music similarity.
{"title":"Mood Classfication from Musical Audio Using User Group-Dependent Models","authors":"Kyogu Lee, Minsuk Cho","doi":"10.1109/ICMLA.2011.96","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.96","url":null,"abstract":"In this paper, we propose a music mood classification system that reflects a user's profile based on a belief that music mood perception is subjective and can vary depending on the user's profile such as age or gender. To this end, we first define a set of generic mood descriptors. Secondly, we make up several user profiles according to the age and gender. We then obtain musical items, for each group, to separately train the statistical models. Using the two different user models, we verify our hypothesis that the user profiles play an important role in mood perception by showing that both models achieve higher classification accuracy when the test data and the mood model are of the same kind. Applying our system to automatic play list generation, we also demonstrate that considering the difference between the user groups in mood perception has a significant effect in computing music similarity.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115165129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
When examining Computed Tomography (CT) scans of lungs for potential abnormalities, radiologists make use of lung nodule's semantic characteristics during the analysis. Computer-Aided Diagnostic Characterization (CADc) systems can act as an aid - predicting ratings of these semantic characteristics to aid radiologists in evaluating the nodule and potentially improve the quality and consistency of diagnosis. In our work, we propose a system for predicting the distribution of radiologists' opinions using a probabilistic multi-class classification approach based on combination of belief decision trees and ADABoost ensemble learning approach. To train and test our system we use the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset, which includes semantic annotations by up to four radiologists for each one of the 914 nodules. Furthermore, we evaluate our probabilistic multi-class classifications using a novel distance-threshold curve technique intended for assessing the performance of uncertain classification systems. We conclude that for the majority of semantic characteristics there exists a set of parameters that significantly improves the performance of the ensemble over the single classifier.
{"title":"Building an Ensemble of Probabilistic Classifiers for Lung Nodule Interpretation","authors":"D. Zinovev, J. Furst, D. Raicu","doi":"10.1109/ICMLA.2011.44","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.44","url":null,"abstract":"When examining Computed Tomography (CT) scans of lungs for potential abnormalities, radiologists make use of lung nodule's semantic characteristics during the analysis. Computer-Aided Diagnostic Characterization (CADc) systems can act as an aid - predicting ratings of these semantic characteristics to aid radiologists in evaluating the nodule and potentially improve the quality and consistency of diagnosis. In our work, we propose a system for predicting the distribution of radiologists' opinions using a probabilistic multi-class classification approach based on combination of belief decision trees and ADABoost ensemble learning approach. To train and test our system we use the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset, which includes semantic annotations by up to four radiologists for each one of the 914 nodules. Furthermore, we evaluate our probabilistic multi-class classifications using a novel distance-threshold curve technique intended for assessing the performance of uncertain classification systems. We conclude that for the majority of semantic characteristics there exists a set of parameters that significantly improves the performance of the ensemble over the single classifier.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117113950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Diego Campos-Sobrino, Francisco Coral-Sabido, Martha Varguez-Moo, Víctor Uc Cetina, A. Espinosa-Romero
We present preliminary results derived from our project on robot self localization using omni directional images. In our approach, features are generated through the computation of covariance matrices that capture important patterns that relates changes in pixel intensities. The learning models used are Mixture of Gaussians and Gaussian Discriminant Analysis. The first method is used initially to test the viability of our feature vectors, and at the same time provides useful information about a natural way of clustering the images in our traning set. Once we determined a reliable set of features, we generated the Gaussian discriminant functions. We show promising experimental results obtained with the Pioneer P3-DX robot in the hallways of the School of Mathematics at the Yucatan Autonomous University in Mexico.
{"title":"Mobile Robot Self-Localization Based on Omnidirectional Vision and Gaussian Models","authors":"Diego Campos-Sobrino, Francisco Coral-Sabido, Martha Varguez-Moo, Víctor Uc Cetina, A. Espinosa-Romero","doi":"10.1109/ICMLA.2011.95","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.95","url":null,"abstract":"We present preliminary results derived from our project on robot self localization using omni directional images. In our approach, features are generated through the computation of covariance matrices that capture important patterns that relates changes in pixel intensities. The learning models used are Mixture of Gaussians and Gaussian Discriminant Analysis. The first method is used initially to test the viability of our feature vectors, and at the same time provides useful information about a natural way of clustering the images in our traning set. Once we determined a reliable set of features, we generated the Gaussian discriminant functions. We show promising experimental results obtained with the Pioneer P3-DX robot in the hallways of the School of Mathematics at the Yucatan Autonomous University in Mexico.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125883801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Learning to rank from examples is an important task in modern Information Retrieval systems like Web search engines, where the large number of available features makes hard to manually devise high-performing ranking functions. This paper presents a novel approach to learning-to-rank, which can natively integrate any target metric with no modifications. The target metric is optimized via maximum-likelihood estimation of a probability distribution over the ranks, which are assumed to follow a Boltzmann distribution. Unlike other approaches in the literature like BoltzRank, this approach does not rely on maximizing the expected value of the target score as a proxy of the optimization of target metric. This has both theoretical and performance advantages as the expected value can not be computed both accurately and efficiently. Furthermore, our model employs the pseudo-likelihood as an accurate surrogate of the likelihood to avoid to explicitly compute the normalization factor of the Boltzmann distribution, which is intractable in this context. The experimental results show that the approach provides state-of-the-art results on various benchmarks and on a dataset built from the logs of a commercial search engine.
{"title":"Learning to Rank Using Markov Random Fields","authors":"Antonino Freno, Tiziano Papini, Michelangelo Diligenti","doi":"10.1109/ICMLA.2011.157","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.157","url":null,"abstract":"Learning to rank from examples is an important task in modern Information Retrieval systems like Web search engines, where the large number of available features makes hard to manually devise high-performing ranking functions. This paper presents a novel approach to learning-to-rank, which can natively integrate any target metric with no modifications. The target metric is optimized via maximum-likelihood estimation of a probability distribution over the ranks, which are assumed to follow a Boltzmann distribution. Unlike other approaches in the literature like BoltzRank, this approach does not rely on maximizing the expected value of the target score as a proxy of the optimization of target metric. This has both theoretical and performance advantages as the expected value can not be computed both accurately and efficiently. Furthermore, our model employs the pseudo-likelihood as an accurate surrogate of the likelihood to avoid to explicitly compute the normalization factor of the Boltzmann distribution, which is intractable in this context. The experimental results show that the approach provides state-of-the-art results on various benchmarks and on a dataset built from the logs of a commercial search engine.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122330806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper introduces a binary large margin classifier that approximates each class with an hyper disk constructed from its training samples. For any pair of classes approximated with hyper disks, there is a corresponding linear separating hyper plane that maximizes the margin between them, and this can be found by solving a convex program that finds the closest pair of points on the hyper disks. More precisely, the best separating hyper plane is chosen to be the one that is orthogonal to the line segment connecting the closest points on the hyper disks and at the same time bisects the line. The method is extended to the nonlinear case by using the kernel trick, and the multi-class classification problems are dealt with constructing and combining several binary classifiers as in Support Vector Machine (SVM) classifier. The experiments on several databases show that the proposed method compares favorably to other popular large margin classifiers.
{"title":"Large Margin Classifier Based on Hyperdisks","authors":"Hakan Cevikalp","doi":"10.1109/ICMLA.2011.86","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.86","url":null,"abstract":"This paper introduces a binary large margin classifier that approximates each class with an hyper disk constructed from its training samples. For any pair of classes approximated with hyper disks, there is a corresponding linear separating hyper plane that maximizes the margin between them, and this can be found by solving a convex program that finds the closest pair of points on the hyper disks. More precisely, the best separating hyper plane is chosen to be the one that is orthogonal to the line segment connecting the closest points on the hyper disks and at the same time bisects the line. The method is extended to the nonlinear case by using the kernel trick, and the multi-class classification problems are dealt with constructing and combining several binary classifiers as in Support Vector Machine (SVM) classifier. The experiments on several databases show that the proposed method compares favorably to other popular large margin classifiers.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128334773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A machine learning methodology, called flow field forecasting, is proposed for statistically predicting the future of a univariate time series. Flow field forecasting draws information from the interpolated flow field of an observed time series to build a forecast step-by-step. Flow field forecasting is presented with examples, a discussion of its properties relative to other common forecasting techniques, and a statistical error analysis.
{"title":"Introducing Flow Field Forecasting","authors":"Michael Frey, Kyle A. Caudle","doi":"10.1109/ICMLA.2011.82","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.82","url":null,"abstract":"A machine learning methodology, called flow field forecasting, is proposed for statistically predicting the future of a univariate time series. Flow field forecasting draws information from the interpolated flow field of an observed time series to build a forecast step-by-step. Flow field forecasting is presented with examples, a discussion of its properties relative to other common forecasting techniques, and a statistical error analysis.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"332 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124675691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Schrom, L. Kangas, Bojana Ginovska-Pangovska, T. Metz, John H. Miller
An artificial neural network is developed for predicting which fragment is charged and which fragment is neutral for lipid fragment pairs produced from a liquid chromatography tandem mass spectrometry simulation process. This charge predictor is integrated into software developed at PNNL for in silico spectra generation and identification of metabolites known as Met ISIS. To test the effect of including charge prediction in Met ISIS, 46 lipids are used which show a reduction in false positive identifications when the charge predictor is utilized.
{"title":"Charge Prediction of Lipid Fragments in Mass Spectrometry","authors":"B. Schrom, L. Kangas, Bojana Ginovska-Pangovska, T. Metz, John H. Miller","doi":"10.1109/ICMLA.2011.45","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.45","url":null,"abstract":"An artificial neural network is developed for predicting which fragment is charged and which fragment is neutral for lipid fragment pairs produced from a liquid chromatography tandem mass spectrometry simulation process. This charge predictor is integrated into software developed at PNNL for in silico spectra generation and identification of metabolites known as Met ISIS. To test the effect of including charge prediction in Met ISIS, 46 lipids are used which show a reduction in false positive identifications when the charge predictor is utilized.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129869140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Considering the special needs of credit risk analysis, the Infinite DEcision Agent ensemble Learning (IDEAL) system is proposed. In the first level of our model, we adopt soft margin boosting to overcome over fitting. In the second level, the RVM algorithm is revised for boosting so that different RVM agents can be generated from the updated instance space of the data. In the third level, the perceptron kernel is employed in RVM to generate infinite subagents. Our IDEAL system also shares some good properties, such as good generalization performance, immunity to over fitting and predicting the distance to default. According to the experimental results, our proposed system can achieve better performance in term of sensitivity, specificity and overall accuracy.
{"title":"Infinite Decision Agent Ensemble Learning System for Credit Risk Analysis","authors":"Shukai Li, I. Tsang, N. Chaudhari","doi":"10.1109/ICMLA.2011.80","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.80","url":null,"abstract":"Considering the special needs of credit risk analysis, the Infinite DEcision Agent ensemble Learning (IDEAL) system is proposed. In the first level of our model, we adopt soft margin boosting to overcome over fitting. In the second level, the RVM algorithm is revised for boosting so that different RVM agents can be generated from the updated instance space of the data. In the third level, the perceptron kernel is employed in RVM to generate infinite subagents. Our IDEAL system also shares some good properties, such as good generalization performance, immunity to over fitting and predicting the distance to default. According to the experimental results, our proposed system can achieve better performance in term of sensitivity, specificity and overall accuracy.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130571511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Signal sequences are practically observed as composites in which a few number of factor signals are linearly combined with non-negative weights. Based on prior physical knowledge about the target, the factors can be modeled as parametric functions, and their parameter values benefit further analyses. In this paper, we present a novel factorization method for the composite signals in terms of parametric factor functions. The method optimizes both the factor weights and the parameter values in the factor functions. While the parameter values are simply optimized by gradient descent, we propose L0-regularized non-negative least squares (L0-NNLS) for optimizing the factor weights. In L0-NNLS, both L0 regularization and non-negativity constraint are imposed on the weights in the least squares to enhance the sparsity as much as possible. Since so regularized least squares is NPhard, we propose a stepwise forward/backward optimization to efficiently solve it in an approximated manner. Due to the sparsity by the L0-NNLS, the proposed factorization method can automatically discover the inherent number of factor functions as well as the parametric functions themselves by estimating their parameter values. In the experiments on factorization of simulated signals and practical biological signals, the proposed method exhibits favorable performances.
{"title":"L0-Regularized Parametric Non-negative Factorization for Analyzing Composite Signals","authors":"Takumi Kobayashi, Kenji Watanabe, N. Otsu","doi":"10.1109/ICMLA.2011.84","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.84","url":null,"abstract":"Signal sequences are practically observed as composites in which a few number of factor signals are linearly combined with non-negative weights. Based on prior physical knowledge about the target, the factors can be modeled as parametric functions, and their parameter values benefit further analyses. In this paper, we present a novel factorization method for the composite signals in terms of parametric factor functions. The method optimizes both the factor weights and the parameter values in the factor functions. While the parameter values are simply optimized by gradient descent, we propose L0-regularized non-negative least squares (L0-NNLS) for optimizing the factor weights. In L0-NNLS, both L0 regularization and non-negativity constraint are imposed on the weights in the least squares to enhance the sparsity as much as possible. Since so regularized least squares is NPhard, we propose a stepwise forward/backward optimization to efficiently solve it in an approximated manner. Due to the sparsity by the L0-NNLS, the proposed factorization method can automatically discover the inherent number of factor functions as well as the parametric functions themselves by estimating their parameter values. In the experiments on factorization of simulated signals and practical biological signals, the proposed method exhibits favorable performances.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130593115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Developing vigorous mathematical models and estimating accurate parameters within feasible computational time are two indispensable parts to build reliable system models for representing biological properties of the system and for producing reliable simulation. For a complex biological system with limited observations, one of the daunting tasks is the large number of unknown parameters in the mathematical modeling whose values directly determine the performance of computational modeling. To tackle this problem, we have developed a data-driven global optimization method, nonlinear RANSAC, based on Random Sample Consensus (a.k.a. RANSAC) method, for parameter estimation of nonlinear system models. Conventional RANSAC method is sound and simple, but it is oriented for linear system models. We not only adopt the strengths of RANSAC, but also extend the method for nonlinear systems with outstanding performance. As a specific application example, we have targeted understanding phagocyte transmigration which is involved in the fibrosis process for biomedical device implantation. With well-defined mathematical nonlinear equations of the system, nonlinear RANSAC is performed for the parameter estimation. Moreover, simulations of the system for propagation prediction over the time are conducted under both normal conditions and knock-out conditions. In order to evaluate the general performance of the method, we also applied the method to signalling pathways where mathematical equations which are representing interaction of proteins are generated using ordinary differential equations as a general format, and public data sets for nonlinear regression evaluation are used to assess its performance.
{"title":"Nonlinear RANSAC Optimization for Parameter Estimation with Applications to Phagocyte Transmigration","authors":"Mingon Kang, Jean X. Gao, Liping Tang","doi":"10.1109/ICMLA.2011.104","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.104","url":null,"abstract":"Developing vigorous mathematical models and estimating accurate parameters within feasible computational time are two indispensable parts to build reliable system models for representing biological properties of the system and for producing reliable simulation. For a complex biological system with limited observations, one of the daunting tasks is the large number of unknown parameters in the mathematical modeling whose values directly determine the performance of computational modeling. To tackle this problem, we have developed a data-driven global optimization method, nonlinear RANSAC, based on Random Sample Consensus (a.k.a. RANSAC) method, for parameter estimation of nonlinear system models. Conventional RANSAC method is sound and simple, but it is oriented for linear system models. We not only adopt the strengths of RANSAC, but also extend the method for nonlinear systems with outstanding performance. As a specific application example, we have targeted understanding phagocyte transmigration which is involved in the fibrosis process for biomedical device implantation. With well-defined mathematical nonlinear equations of the system, nonlinear RANSAC is performed for the parameter estimation. Moreover, simulations of the system for propagation prediction over the time are conducted under both normal conditions and knock-out conditions. In order to evaluate the general performance of the method, we also applied the method to signalling pathways where mathematical equations which are representing interaction of proteins are generated using ordinary differential equations as a general format, and public data sets for nonlinear regression evaluation are used to assess its performance.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"25 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114114388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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