Canonical correlation analysis (CCA) has been widely used in pattern recognition and machine learning. However, both CCA and its extensions sometimes cannot give satisfactory results. In this paper, we propose a new CCA-type method termed sparse representation based discriminative CCA (SPDCCA) by incorporating sparse representation and discriminative information simultaneously into traditional CCA. In particular, SPDCCA not only preserves the sparse reconstruction relationship within data based on sparse representation, but also preserves the maximum-margin based discriminative information, and thus it further enhances the classification performance. Experimental results on Yale, Extended Yale B, and ORL datasets show that SPDCCA outperforms both CCA and its extensions including KCCA, LPCCA and LDCCA in face recognition.
{"title":"Sparse Representation Based Discriminative Canonical Correlation Analysis for Face Recognition","authors":"Naiyang Guan, Xiang Zhang, Zhigang Luo, L. Lan","doi":"10.1109/ICMLA.2012.18","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.18","url":null,"abstract":"Canonical correlation analysis (CCA) has been widely used in pattern recognition and machine learning. However, both CCA and its extensions sometimes cannot give satisfactory results. In this paper, we propose a new CCA-type method termed sparse representation based discriminative CCA (SPDCCA) by incorporating sparse representation and discriminative information simultaneously into traditional CCA. In particular, SPDCCA not only preserves the sparse reconstruction relationship within data based on sparse representation, but also preserves the maximum-margin based discriminative information, and thus it further enhances the classification performance. Experimental results on Yale, Extended Yale B, and ORL datasets show that SPDCCA outperforms both CCA and its extensions including KCCA, LPCCA and LDCCA in face recognition.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131899029","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}
Discrete Event Systems (DES) are dynamical systems that evolve according to the asynchronous occurrence of certain changes called events. This paper proposes a modular approach for abrupt and drift-like fault diagnosis of concurrent DES. In this class of DES, the system consists of several components or subsystems that operate concurrently. Each component is modeled as a sequence of predetermined actions as well as the responses to these actions. Each component model represents the desired (nominal) system behavior. An abrupt fault is viewed as a violation of the component desired behavior. While a drift-like fault is viewed as a drift in the normal characteristics of component response to actions. An indicator measuring the change in the response characteristics of the component is used to detect a drift. This detection can be then used to warn a human operator when the component behavior starts to deviate from its normal behavior. The proposed approach is illustrated using a manufacturing system.
{"title":"Abrupt and Drift-Like Fault Diagnosis of Concurent Discrete Event Systems","authors":"M. S. Mouchaweh, P. Billaudel","doi":"10.1109/ICMLA.2012.157","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.157","url":null,"abstract":"Discrete Event Systems (DES) are dynamical systems that evolve according to the asynchronous occurrence of certain changes called events. This paper proposes a modular approach for abrupt and drift-like fault diagnosis of concurrent DES. In this class of DES, the system consists of several components or subsystems that operate concurrently. Each component is modeled as a sequence of predetermined actions as well as the responses to these actions. Each component model represents the desired (nominal) system behavior. An abrupt fault is viewed as a violation of the component desired behavior. While a drift-like fault is viewed as a drift in the normal characteristics of component response to actions. An indicator measuring the change in the response characteristics of the component is used to detect a drift. This detection can be then used to warn a human operator when the component behavior starts to deviate from its normal behavior. The proposed approach is illustrated using a manufacturing system.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"140 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129652240","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}
Formal model verification has proven a powerful tool for verifying and validating the properties of a system. Central to this class of techniques is the construction of an accurate formal model for the system being investigated. Unfortunately, manual construction of such models can be a resource demanding process, and this shortcoming has motivated the development of algorithms for automatically learning system models from observed system behaviors. Recently, algorithms have been proposed for learning Markov decision process representations of reactive systems based on alternating sequences of input/output observations. While alleviating the problem of manually constructing a system model, the collection/generation of observed system behaviors can also prove demanding. Consequently we seek to minimize the amount of data required. In this paper we propose an algorithm for learning deterministic Markov decision processes from data by actively guiding the selection of input actions. The algorithm is empirically analyzed by learning system models of slot machines, and it is demonstrated that the proposed active learning procedure can significantly reduce the amount of data required to obtain accurate system models.
{"title":"Active Learning of Markov Decision Processes for System Verification","authors":"Yingke Chen, Thomas D. Nielsen","doi":"10.1109/ICMLA.2012.158","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.158","url":null,"abstract":"Formal model verification has proven a powerful tool for verifying and validating the properties of a system. Central to this class of techniques is the construction of an accurate formal model for the system being investigated. Unfortunately, manual construction of such models can be a resource demanding process, and this shortcoming has motivated the development of algorithms for automatically learning system models from observed system behaviors. Recently, algorithms have been proposed for learning Markov decision process representations of reactive systems based on alternating sequences of input/output observations. While alleviating the problem of manually constructing a system model, the collection/generation of observed system behaviors can also prove demanding. Consequently we seek to minimize the amount of data required. In this paper we propose an algorithm for learning deterministic Markov decision processes from data by actively guiding the selection of input actions. The algorithm is empirically analyzed by learning system models of slot machines, and it is demonstrated that the proposed active learning procedure can significantly reduce the amount of data required to obtain accurate system models.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130863882","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}
Shiming Yang, K. Kalpakis, C. Mackenzie, L. Stansbury, D. Stein, T. Scalea, P. Hu
Continuous, automated, electronic patient vital signs data are important to physicians in evaluating traumatic brain injury (TBI) patients' physiological status and reaching timely decisions for therapeutic interventions. However, missing values in the medical data streams hinder applying many standard statistical or machine learning algorithms and result in losing some episodes of clinical importance. In this paper, we present a novel approach to filling missing values in streams of vital signs data. We construct sequences of Hankel matrices from vital signs data streams, find that these matrices exhibit low-rank, and utilize low-rank matrix completion methods from compressible sensing to fill in the missing data. We demonstrate that our approach always substantially outperforms other popular fill-in methods, like k-nearest-neighbors and expectation maximization. Further, we show that our approach recovers thousands of simulated missing data for intracranial pressure, a critical stream of measurements for guiding clinical interventions and monitoring traumatic brain injuries.
{"title":"Online Recovery of Missing Values in Vital Signs Data Streams Using Low-Rank Matrix Completion","authors":"Shiming Yang, K. Kalpakis, C. Mackenzie, L. Stansbury, D. Stein, T. Scalea, P. Hu","doi":"10.1109/ICMLA.2012.55","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.55","url":null,"abstract":"Continuous, automated, electronic patient vital signs data are important to physicians in evaluating traumatic brain injury (TBI) patients' physiological status and reaching timely decisions for therapeutic interventions. However, missing values in the medical data streams hinder applying many standard statistical or machine learning algorithms and result in losing some episodes of clinical importance. In this paper, we present a novel approach to filling missing values in streams of vital signs data. We construct sequences of Hankel matrices from vital signs data streams, find that these matrices exhibit low-rank, and utilize low-rank matrix completion methods from compressible sensing to fill in the missing data. We demonstrate that our approach always substantially outperforms other popular fill-in methods, like k-nearest-neighbors and expectation maximization. Further, we show that our approach recovers thousands of simulated missing data for intracranial pressure, a critical stream of measurements for guiding clinical interventions and monitoring traumatic brain injuries.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133654176","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}
Boosting methods have been successfully applied in a wide variety of machine learning applications. In the context of data quality issues, a number of variants of the standard boosting method have been proposed and evaluated. To address the problem of mislabeled examples, ORBoost was developed to prevent over fitting to noisy examples. Our research group has recently proposed RUSBoost as an enhancement to the AdaBoost algorithm for dealing with skewed class distributions. This work proposes a modification to the RUSBoost algorithm, incorporating the noise-handling ability of ORBoost, to improve its handling of noisy data. The new method is compared with both ORBoost and RUSBoost in an extensive set of experiments using five real-world datasets with various levels of simulated noise.
{"title":"A Novel Noise-Resistant Boosting Algorithm for Class-Skewed Data","authors":"J. V. Hulse, T. Khoshgoftaar, Amri Napolitano","doi":"10.1109/ICMLA.2012.153","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.153","url":null,"abstract":"Boosting methods have been successfully applied in a wide variety of machine learning applications. In the context of data quality issues, a number of variants of the standard boosting method have been proposed and evaluated. To address the problem of mislabeled examples, ORBoost was developed to prevent over fitting to noisy examples. Our research group has recently proposed RUSBoost as an enhancement to the AdaBoost algorithm for dealing with skewed class distributions. This work proposes a modification to the RUSBoost algorithm, incorporating the noise-handling ability of ORBoost, to improve its handling of noisy data. The new method is compared with both ORBoost and RUSBoost in an extensive set of experiments using five real-world datasets with various levels of simulated noise.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116259581","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}
Central pattern generator (CPG) is a kind of neural network which is located in the spinal cord. It has been found to be responsible for many rhythmic biological movements, such as breathing, swimming, flying as well as walking. Many CPG models have been designed and proved to be useful. But the CPG outputs of these models are often sine waves or quasi-sine waves. Also these outputs are directly used as the control signals to control joint trajectories or joint torques on robots. This is obviously not an accurate design in robot walking control especially when sine or quasisine waves are not the best signals to set walking patters because of the complexity of tasks. In this paper, based on the idea of Righetti, Buchli and Ijspeert, a CPG model is designed, which is inspired by Fourier series and can produce outputs with any shape. There are a limited set of sub-components in the proposed model. Each sub-component learns one harmonic of a reference wave. A summation of these sub-components is used to approximate the wave. In this way, the wave will be learned and embedded in the CPG model. In the proposed model, FFT is used to see the harmonics and calculate the frequency. The system is designed in polar coordinates with new Hebbian learning items and Kuramoto model items. Because the whole system is a limit cycle system, it is robust to perturbation. The experiment conducted on an AIBO robot shows the effectiveness of the proposed model.
{"title":"A Series Inspired CPG Model for Robot Walking Control","authors":"Jiaqi Zhang, Xianchao Zhao, Chenkun Qi","doi":"10.1109/ICMLA.2012.80","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.80","url":null,"abstract":"Central pattern generator (CPG) is a kind of neural network which is located in the spinal cord. It has been found to be responsible for many rhythmic biological movements, such as breathing, swimming, flying as well as walking. Many CPG models have been designed and proved to be useful. But the CPG outputs of these models are often sine waves or quasi-sine waves. Also these outputs are directly used as the control signals to control joint trajectories or joint torques on robots. This is obviously not an accurate design in robot walking control especially when sine or quasisine waves are not the best signals to set walking patters because of the complexity of tasks. In this paper, based on the idea of Righetti, Buchli and Ijspeert, a CPG model is designed, which is inspired by Fourier series and can produce outputs with any shape. There are a limited set of sub-components in the proposed model. Each sub-component learns one harmonic of a reference wave. A summation of these sub-components is used to approximate the wave. In this way, the wave will be learned and embedded in the CPG model. In the proposed model, FFT is used to see the harmonics and calculate the frequency. The system is designed in polar coordinates with new Hebbian learning items and Kuramoto model items. Because the whole system is a limit cycle system, it is robust to perturbation. The experiment conducted on an AIBO robot shows the effectiveness of the proposed model.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116724747","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}
In the clinical diagnosis of facial dysmorphology, geneticists attempt to identify the underlying syndromes by associating facial features before cyto or molecular techniques are explored. Specifying genotype-phenotype correlations correctly among many syndromes is labor intensive especially for very rare diseases. The use of a computer based prediagnosis system can offer effective decision support particularly when only very few previous examples exist or in a remote environment where expert knowledge is not readily accessible. In this work we develop and demonstrate that accurate classification of dysmorphic faces is feasible by image processing of two dimensional face images. We test the proposed system on real patient image data by constructing a dataset of dysmorphic faces published in scholarly journals, hence having accurate diagnostic information about the syndrome. Our statistical methodology represents facial image data in terms of principal component analysis (PCA) and a leave one out evaluation scheme to quantify accuracy. The methodology has been tested with 15 syndromes including 75 cases, 5 examples per syndrome. A diagnosis success rate of 79% has been established. It can be concluded that a great number of syndromes indicating a characteristic pattern of facial anomalies can be typically diagnosed by employing computer-assisted machine learning algorithms since a face develops under the influence of many genes, particularly the genes causing syndromes.
{"title":"Establishment of a Diagnostic Decision Support System in Genetic Dysmorphology","authors":"Kaya Kuru, M. Niranjan, Y. Tunca","doi":"10.1109/ICMLA.2012.234","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.234","url":null,"abstract":"In the clinical diagnosis of facial dysmorphology, geneticists attempt to identify the underlying syndromes by associating facial features before cyto or molecular techniques are explored. Specifying genotype-phenotype correlations correctly among many syndromes is labor intensive especially for very rare diseases. The use of a computer based prediagnosis system can offer effective decision support particularly when only very few previous examples exist or in a remote environment where expert knowledge is not readily accessible. In this work we develop and demonstrate that accurate classification of dysmorphic faces is feasible by image processing of two dimensional face images. We test the proposed system on real patient image data by constructing a dataset of dysmorphic faces published in scholarly journals, hence having accurate diagnostic information about the syndrome. Our statistical methodology represents facial image data in terms of principal component analysis (PCA) and a leave one out evaluation scheme to quantify accuracy. The methodology has been tested with 15 syndromes including 75 cases, 5 examples per syndrome. A diagnosis success rate of 79% has been established. It can be concluded that a great number of syndromes indicating a characteristic pattern of facial anomalies can be typically diagnosed by employing computer-assisted machine learning algorithms since a face develops under the influence of many genes, particularly the genes causing syndromes.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134084955","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 describes a nonlinear face recognition method based on polynomial spatial frequency image processing. This nonlinear method is known as the polynomial distance classifier correlation filter (PDCCF). PDCCF is a member of a well-known family of filters called correlation filters. Correlation filters are attractive because of their shift invariance and potential for distortion tolerant pattern recognition. PDCCF addresses more than one filter in the system, each one with a different form of non-linearity. Our experimental results on the Olivetti Research Laboratory (ORL) and Extended Yale B (EYB) face datasets show that PDCCF outperforms the principal component analysis (PCA), and the local binary pattern (LBP).
提出了一种基于多项式空间频率图像处理的非线性人脸识别方法。这种非线性方法被称为多项式距离分类器相关滤波器(PDCCF)。PDCCF是众所周知的相关滤波器家族中的一员。相关滤波器因其移位不变性和抗畸变模式识别的潜力而备受关注。PDCCF处理系统中的多个滤波器，每个滤波器都具有不同形式的非线性。我们在Olivetti Research Laboratory (ORL)和Extended Yale B (EYB)人脸数据集上的实验结果表明，PDCCF优于主成分分析(PCA)和局部二值模式(LBP)。
{"title":"Polynomial Correlation Filters for Human Face Recognition","authors":"Mohamed I. Alkanhal, Muhammad Ghulam","doi":"10.1109/ICMLA.2012.120","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.120","url":null,"abstract":"This paper describes a nonlinear face recognition method based on polynomial spatial frequency image processing. This nonlinear method is known as the polynomial distance classifier correlation filter (PDCCF). PDCCF is a member of a well-known family of filters called correlation filters. Correlation filters are attractive because of their shift invariance and potential for distortion tolerant pattern recognition. PDCCF addresses more than one filter in the system, each one with a different form of non-linearity. Our experimental results on the Olivetti Research Laboratory (ORL) and Extended Yale B (EYB) face datasets show that PDCCF outperforms the principal component analysis (PCA), and the local binary pattern (LBP).","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133439199","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}
Text clustering is used on a variety of applications such as content-based recommendation, categorization, summarization, information retrieval and automatic topic extraction. Since most pair of documents usually shares just a small percentage of words, the dataset representation tends to become very sparse, thus the need of using a similarity metric capable of a partial matching of a set of features. The technique known as Co-Clustering is capable of finding several clusters inside a dataset with each cluster composed of just a subset of the object and feature sets. In word-document data this can be useful to identify the clusters of documents pertaining to the same topic, even though they share just a small fraction of words. In this paper a scalable co-clustering algorithm is proposed using the Locality-sensitive hashing technique in order to find co-clusters of documents. The proposed algorithm will be tested against other co-clustering and traditional algorithms in well known datasets. The results show that this algorithm is capable of finding clusters more accurately than other approaches while maintaining a linear complexity.
{"title":"Scalable Overlapping Co-clustering of Word-Document Data","authors":"F. O. França","doi":"10.1109/ICMLA.2012.84","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.84","url":null,"abstract":"Text clustering is used on a variety of applications such as content-based recommendation, categorization, summarization, information retrieval and automatic topic extraction. Since most pair of documents usually shares just a small percentage of words, the dataset representation tends to become very sparse, thus the need of using a similarity metric capable of a partial matching of a set of features. The technique known as Co-Clustering is capable of finding several clusters inside a dataset with each cluster composed of just a subset of the object and feature sets. In word-document data this can be useful to identify the clusters of documents pertaining to the same topic, even though they share just a small fraction of words. In this paper a scalable co-clustering algorithm is proposed using the Locality-sensitive hashing technique in order to find co-clusters of documents. The proposed algorithm will be tested against other co-clustering and traditional algorithms in well known datasets. The results show that this algorithm is capable of finding clusters more accurately than other approaches while maintaining a linear complexity.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129347638","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}
Xiaoguang Wang, Hang Shao, N. Japkowicz, S. Matwin, Xuan Liu, A. Bourque, Bao Nguyen
Real world data mining applications such as Mine Countermeasure Missions (MCM) involve learning from imbalanced data sets, which contain very few instances of the minority classes and many instances of the majority class. For instance, the number of naturally occurring clutter objects (such as rocks) that are detected typically far outweighs the relatively rare event of detecting a mine. In this paper we propose support vector machine with adaptive asymmetric misclassification costs (instances weighted) to solve the skewed vector spaces problem in mine countermeasure missions. Experimental results show that the given algorithm could be used for imbalanced sonar image data sets and makes an improvement in prediction performance.
{"title":"Using SVM with Adaptively Asymmetric MisClassification Costs for Mine-Like Objects Detection","authors":"Xiaoguang Wang, Hang Shao, N. Japkowicz, S. Matwin, Xuan Liu, A. Bourque, Bao Nguyen","doi":"10.1109/ICMLA.2012.227","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.227","url":null,"abstract":"Real world data mining applications such as Mine Countermeasure Missions (MCM) involve learning from imbalanced data sets, which contain very few instances of the minority classes and many instances of the majority class. For instance, the number of naturally occurring clutter objects (such as rocks) that are detected typically far outweighs the relatively rare event of detecting a mine. In this paper we propose support vector machine with adaptive asymmetric misclassification costs (instances weighted) to solve the skewed vector spaces problem in mine countermeasure missions. Experimental results show that the given algorithm could be used for imbalanced sonar image data sets and makes an improvement in prediction performance.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116062203","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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