Real-time burst detection over multiple window size is useful for analyzing data streams. Various burst detection methods have been proposed. However, they are not effective for real-time detection. This work proposes a new burst detection method that reduces computation by avoiding redundant data updates. It analyses an event on its occurrence, and detects the period where arrival frequency rises rapidly to the previous period. In addition, it reduces computation by suppressing data within a certain period even in the case of emergent increase of events. The effectiveness of the proposed method is evaluated by experiments with real data.
{"title":"A Real-Time Burst Detection Method","authors":"Ryohei Ebina, Kenji Nakamura, S. Oyanagi","doi":"10.1109/ICTAI.2011.177","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.177","url":null,"abstract":"Real-time burst detection over multiple window size is useful for analyzing data streams. Various burst detection methods have been proposed. However, they are not effective for real-time detection. This work proposes a new burst detection method that reduces computation by avoiding redundant data updates. It analyses an event on its occurrence, and detects the period where arrival frequency rises rapidly to the previous period. In addition, it reduces computation by suppressing data within a certain period even in the case of emergent increase of events. The effectiveness of the proposed method is evaluated by experiments with real data.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121347129","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}
The paper proposes an Evolutionary-based method to improve the prediction performance of Support Vector Machines classifiers applied to both artificial and real-world datasets which suffer from the curse of dimensionality. This method performs a simultaneous feature and model selection to discover the subset of features and the SVM parameters' values which provide a low prediction error. Moreover, it does not require a pre-processing step to filter the features so it can be applied to a whole dataset.
{"title":"Simultaneous Feature and Model Selection for High-Dimensional Data","authors":"A. Perolini, S. Guérif","doi":"10.1109/ICTAI.2011.16","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.16","url":null,"abstract":"The paper proposes an Evolutionary-based method to improve the prediction performance of Support Vector Machines classifiers applied to both artificial and real-world datasets which suffer from the curse of dimensionality. This method performs a simultaneous feature and model selection to discover the subset of features and the SVM parameters' values which provide a low prediction error. Moreover, it does not require a pre-processing step to filter the features so it can be applied to a whole dataset.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127701194","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}
V. R. Borges, C. Barcelos, D. Guliato, M. A. Batista
This paper presents a multiphase image segmentation model based on Fuzzy Region Competition. The proposed model approximates image regions by probability density functions and uses a supervised approach in the segmentation process. The strategy of the proposed model is to perform two-phase Fuzzy Region Competition model several times, where a hard partition is obtained in each round from the determined fuzzy membership function. Consequently, the segmentation process is soft, while the final result is hard, given the simplicity of avoiding non-overlapping and vacuum regions. The proposed model was validated using multiphase images, which showed to be robust under the presence of noise and presented good accuracy when dealing with texturized and natural images.
{"title":"A Selective Fuzzy Region Competition Model for Multiphase Image Segmentation","authors":"V. R. Borges, C. Barcelos, D. Guliato, M. A. Batista","doi":"10.1109/ICTAI.2011.26","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.26","url":null,"abstract":"This paper presents a multiphase image segmentation model based on Fuzzy Region Competition. The proposed model approximates image regions by probability density functions and uses a supervised approach in the segmentation process. The strategy of the proposed model is to perform two-phase Fuzzy Region Competition model several times, where a hard partition is obtained in each round from the determined fuzzy membership function. Consequently, the segmentation process is soft, while the final result is hard, given the simplicity of avoiding non-overlapping and vacuum regions. The proposed model was validated using multiphase images, which showed to be robust under the presence of noise and presented good accuracy when dealing with texturized and natural images.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"341 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122272832","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}
Most realistic environments are complex, partially observable and impose real-time constraints on agents operating within them. This paper describes a framework that allows agents to learn by observation in such environments. When learning by observation, agents observe an expert performing a task and learn to perform the same task based on those observations. Our framework aims to allow agents to learn in a variety of domains (physical or virtual) regardless of the behaviour or goals of the observed expert. To achieve this we ensure that there is a clear separation between the central reasoning system and any domain-specific information. We present case studies in the domains of obstacle avoidance, robotic arm control, simulated soccer and Tetris.
{"title":"A Case-Based Reasoning Framework for Developing Agents Using Learning by Observation","authors":"Michael W. Floyd, B. Esfandiari","doi":"10.1109/ICTAI.2011.86","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.86","url":null,"abstract":"Most realistic environments are complex, partially observable and impose real-time constraints on agents operating within them. This paper describes a framework that allows agents to learn by observation in such environments. When learning by observation, agents observe an expert performing a task and learn to perform the same task based on those observations. Our framework aims to allow agents to learn in a variety of domains (physical or virtual) regardless of the behaviour or goals of the observed expert. To achieve this we ensure that there is a clear separation between the central reasoning system and any domain-specific information. We present case studies in the domains of obstacle avoidance, robotic arm control, simulated soccer and Tetris.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121587389","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}
The problem that we address in this paper is that of learning hierarchical object categories. Indeed, Digital media technology generates huge amount of non-textual information. Categorizing this information is a challenging task which has served important applications. An important part of this nontextual information is composed of images and videos which consists of various objects each of which may be used to effectively classify the images or videos. Object classification in computer vision can be looked upon from several different perspectives. From the structural perspective object classification models can be divided into flat and hierarchical models. Many of the well-known hierarchical structures proposed so far are based on the Dirichlet distribution. In this work, however, we present a generative hierarchical statistical model based on generalized Dirichlet distribution for the categorization of visual objects modeled as a set of local features describing patches detected using interest points detector. We demonstrate the effectiveness of the proposed model through extensive experiments.
{"title":"An Expandable Hierarchical Statistical Framework for Count Data Modeling and Its Application to Object Classification","authors":"A. Bakhtiari, N. Bouguila","doi":"10.1109/ICTAI.2011.128","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.128","url":null,"abstract":"The problem that we address in this paper is that of learning hierarchical object categories. Indeed, Digital media technology generates huge amount of non-textual information. Categorizing this information is a challenging task which has served important applications. An important part of this nontextual information is composed of images and videos which consists of various objects each of which may be used to effectively classify the images or videos. Object classification in computer vision can be looked upon from several different perspectives. From the structural perspective object classification models can be divided into flat and hierarchical models. Many of the well-known hierarchical structures proposed so far are based on the Dirichlet distribution. In this work, however, we present a generative hierarchical statistical model based on generalized Dirichlet distribution for the categorization of visual objects modeled as a set of local features describing patches detected using interest points detector. We demonstrate the effectiveness of the proposed model through extensive experiments.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129983394","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}
We propose ConArg, a tool based on Constraint Programming, to model and solve various problems related to the Argumentation research field. Constraint Satisfaction Problems (CSPs) offer a wide number of efficient techniques (as inference and search algorithms) that can tackle the complexity in finding all the possible Dung's conflict-free, admissible, complete, stable, preferred and grounded extensions in Argumentation Frameworks. Moreover, we can use the tool to solve some computationally hard problems presented in [1]. To implement ConArg, we have used JaCoP, a Java library which provides the user with a Finite Domain Constraint Programming paradigm, to model and solve these two problems. ConArg is able to randomly generate two different kinds of small-world networks in order to find Dung's extensions on such interaction graphs. We present the main features of ConArg and the reported performance in time.
{"title":"ConArg: A Constraint-Based Computational Framework for Argumentation Systems","authors":"Stefano Bistarelli, Francesco Santini","doi":"10.1109/ICTAI.2011.96","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.96","url":null,"abstract":"We propose ConArg, a tool based on Constraint Programming, to model and solve various problems related to the Argumentation research field. Constraint Satisfaction Problems (CSPs) offer a wide number of efficient techniques (as inference and search algorithms) that can tackle the complexity in finding all the possible Dung's conflict-free, admissible, complete, stable, preferred and grounded extensions in Argumentation Frameworks. Moreover, we can use the tool to solve some computationally hard problems presented in [1]. To implement ConArg, we have used JaCoP, a Java library which provides the user with a Finite Domain Constraint Programming paradigm, to model and solve these two problems. ConArg is able to randomly generate two different kinds of small-world networks in order to find Dung's extensions on such interaction graphs. We present the main features of ConArg and the reported performance in time.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130006891","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}
Active rules are the mechanism by which some systems can behave automatically. Rule validation is a mandatory step to guarantee those systems work properly. One of the most used validation techniques is based on test cases. In this paper we introduce a new metric through the Conditional Colored Petri Net model of the rule base, to determine the number of test cases.
{"title":"A Petri Net-Based Metric for Active Rule Validation","authors":"Lorena Chavarría-Báez, Xiaoou Li","doi":"10.1109/ICTAI.2011.156","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.156","url":null,"abstract":"Active rules are the mechanism by which some systems can behave automatically. Rule validation is a mandatory step to guarantee those systems work properly. One of the most used validation techniques is based on test cases. In this paper we introduce a new metric through the Conditional Colored Petri Net model of the rule base, to determine the number of test cases.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130123515","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}
An ocean turbine extracts the kinetic energy from ocean currents to generate electricity. Vibration signals from the turbine hold a wealth of information regarding its state, and detecting changes in these signals is crucial to the timely detection of faults. Wavelet transforms provide a means of analyzing these complex signals and extracting features which are representative of the signal. Feature selection techniques are needed once these wavelet features are extracted to eliminate redundant or useless features before the data is presented to a machine learning algorithm for pattern recognition and classification. This reduces the quantity of data to be processed and can often even increase the machine learner's ability to detect the current state of the machine. This paper empirically compares eight feature selection algorithms on wavelet transformed vibration data originating from an onshore test platform for an ocean turbine. A case study shows the classification performances of seven machine learners when trained on the datasets with varying numbers of features selected from the original set of all features. Our results highlight that by choosing an appropriate feature selection technique and applying it to selecting just the 3 most important features (3.33% of the original feature set), some classifiers such as the decision tree and random forest can correctly differentiate between faulty and nonfaulty states almost 100% of the time. These results also show the performance differences between different feature selection algorithms and classifier combinations.
{"title":"Feature Selection on Dynamometer Data for Reliability Analysis","authors":"Janell Duhaney, T. Khoshgoftaar, J. Sloan","doi":"10.1109/ICTAI.2011.173","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.173","url":null,"abstract":"An ocean turbine extracts the kinetic energy from ocean currents to generate electricity. Vibration signals from the turbine hold a wealth of information regarding its state, and detecting changes in these signals is crucial to the timely detection of faults. Wavelet transforms provide a means of analyzing these complex signals and extracting features which are representative of the signal. Feature selection techniques are needed once these wavelet features are extracted to eliminate redundant or useless features before the data is presented to a machine learning algorithm for pattern recognition and classification. This reduces the quantity of data to be processed and can often even increase the machine learner's ability to detect the current state of the machine. This paper empirically compares eight feature selection algorithms on wavelet transformed vibration data originating from an onshore test platform for an ocean turbine. A case study shows the classification performances of seven machine learners when trained on the datasets with varying numbers of features selected from the original set of all features. Our results highlight that by choosing an appropriate feature selection technique and applying it to selecting just the 3 most important features (3.33% of the original feature set), some classifiers such as the decision tree and random forest can correctly differentiate between faulty and nonfaulty states almost 100% of the time. These results also show the performance differences between different feature selection algorithms and classifier combinations.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130166587","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 this paper, we introduce for the qualitative constraint networks (QCNs) a new consistency: the partial weak composition consistency. The partial weak composition consistency, similarly to the partial path-consistency, considers triangles of a graph and corresponds to the weak composition consistency restricted to these triangles. We show that for the pre-convex QCNs of the Interval Algebra (IA), the partial weak composition consistency with respect to a triangulation of the graph of constraints is sufficient to decide the consistency problem. From this result, we propose an algorithm allowing to solve QCNs of IA. The experiments that we have conducted show the interest of this algorithm to solve the consistency problem of the QCNs of IA.
{"title":"Consistency of Triangulated Temporal Qualitative Constraint Networks","authors":"A. Chmeiss, Jean-François Condotta","doi":"10.1109/ICTAI.2011.125","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.125","url":null,"abstract":"In this paper, we introduce for the qualitative constraint networks (QCNs) a new consistency: the partial weak composition consistency. The partial weak composition consistency, similarly to the partial path-consistency, considers triangles of a graph and corresponds to the weak composition consistency restricted to these triangles. We show that for the pre-convex QCNs of the Interval Algebra (IA), the partial weak composition consistency with respect to a triangulation of the graph of constraints is sufficient to decide the consistency problem. From this result, we propose an algorithm allowing to solve QCNs of IA. The experiments that we have conducted show the interest of this algorithm to solve the consistency problem of the QCNs of IA.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130275169","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}
Building powerful machine-learning assessment models is an important achievement of empirical software engineering research, but it is not the only one. Intelligibility of such models is also needed, especially, in a domain, software engineering, where exploration and knowledge capture is still a challenge. Several algorithms, belonging to various machine-learning approaches, are selected and run on software data collected from medium size applications. Some of these approaches produce models with very high quantitative performances, others give interpretable, intelligible, and "glass-box" models that are very complementary. We consider that the integration of both, in automated decision-making systems for assessing software product quality, is desirable to reach a compromise between performance and intelligibility.
{"title":"Machine-Learning Models for Software Quality: A Compromise between Performance and Intelligibility","authors":"H. Lounis, T. Gayed, M. Boukadoum","doi":"10.1109/ICTAI.2011.155","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.155","url":null,"abstract":"Building powerful machine-learning assessment models is an important achievement of empirical software engineering research, but it is not the only one. Intelligibility of such models is also needed, especially, in a domain, software engineering, where exploration and knowledge capture is still a challenge. Several algorithms, belonging to various machine-learning approaches, are selected and run on software data collected from medium size applications. Some of these approaches produce models with very high quantitative performances, others give interpretable, intelligible, and \"glass-box\" models that are very complementary. We consider that the integration of both, in automated decision-making systems for assessing software product quality, is desirable to reach a compromise between performance and intelligibility.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134121146","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：