David J. Miller, Chu-Fang Lin, G. Kesidis, Christopher M. Collins
We have recently introduced new generative semi supervised mixtures with more fine-grained class label generation mechanisms than previous methods. Our models combine advantages of semi supervised mixtures, which achieve label extrapolation over a component, and nearest-neighbor (NN)/nearest-prototype (NP) classification, which achieves accurate classification in the vicinity of labeled samples. Our models are advantageous when within-component class proportions are not constant over the feature space region ``owned by'' a component. In this paper, we develop an active learning extension of our fine-grained labeling methods. We propose two new uncertainty sampling methods in comparison with traditional entropy-based uncertainty sampling. Our experiments on a number of UC Irvine data sets show that the proposed active learning methods improve classification accuracy more than standard entropy-based active learning. The proposed methods are particularly advantageous when the labeled percentage is small. We also extend our semi supervised method to allow variable weighting on labeled and unlabeled data likelihood terms. This approach is shown to outperform previous weighting schemes.
{"title":"Improved Fine-Grained Component-Conditional Class Labeling with Active Learning","authors":"David J. Miller, Chu-Fang Lin, G. Kesidis, Christopher M. Collins","doi":"10.1109/ICMLA.2010.8","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.8","url":null,"abstract":"We have recently introduced new generative semi supervised mixtures with more fine-grained class label generation mechanisms than previous methods. Our models combine advantages of semi supervised mixtures, which achieve label extrapolation over a component, and nearest-neighbor (NN)/nearest-prototype (NP) classification, which achieves accurate classification in the vicinity of labeled samples. Our models are advantageous when within-component class proportions are not constant over the feature space region ``owned by'' a component. In this paper, we develop an active learning extension of our fine-grained labeling methods. We propose two new uncertainty sampling methods in comparison with traditional entropy-based uncertainty sampling. Our experiments on a number of UC Irvine data sets show that the proposed active learning methods improve classification accuracy more than standard entropy-based active learning. The proposed methods are particularly advantageous when the labeled percentage is small. We also extend our semi supervised method to allow variable weighting on labeled and unlabeled data likelihood terms. This approach is shown to outperform previous weighting schemes.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120957159","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 the use of hardware implementation of a real time neural network controller set for reactive power compensation (RPC) systems with synchronous motor. In this study, measurement of parameters required in systems such as current, phase differences, frequency and power are measured by means of a PIC 18F452 microcontroller with high accuracy and then controlled via artificial neural networks;. The performance test based on obtained data using a computer codes written in Visual Basic.Net are implemented. Different ANN controller structures are verified by simulating them on a computer. It is evaluated that the set developed can be easily adapted in real time applications.
{"title":"Hardware Implementation of a Real-Time Neural Network Controller Set for Reactive Power Compensation Systems","authors":"R. Bayindir, Alper Gorgun","doi":"10.1109/ICMLA.2010.107","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.107","url":null,"abstract":"This paper introduces the use of hardware implementation of a real time neural network controller set for reactive power compensation (RPC) systems with synchronous motor. In this study, measurement of parameters required in systems such as current, phase differences, frequency and power are measured by means of a PIC 18F452 microcontroller with high accuracy and then controlled via artificial neural networks;. The performance test based on obtained data using a computer codes written in Visual Basic.Net are implemented. Different ANN controller structures are verified by simulating them on a computer. It is evaluated that the set developed can be easily adapted in real time applications.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126731760","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 of the traditional text classification methods employ Bag of Words (BOW) approaches relying on the words frequencies existing within the training corpus and the testing documents. Recently, studies have examined using external knowledge to enrich the text representation of documents. Some have focused on using WordNet which suffers from different limitations including the available number of words, synsets and coverage. Other studies used different aspects of Wikipedia instead. Depending on the features being selected and evaluated and the external knowledge being used, a balance between recall, precision, noise reduction and information loss has to be applied. In this paper, we propose a new Centroid-based classification approach relying on Wikipedia to enrich the representation of documents through the use of Wikpedia’s concepts, categories structure, links, and articles text. We extract candidate concepts for each class with the help of Wikipedia and merge them with important features derived directly from the text documents. Different variations of the system were evaluated and the results show improvements in the performance of the system.
{"title":"Centroid-based Classification Enhanced with Wikipedia","authors":"Abdullah Bawakid, M. Oussalah","doi":"10.1109/ICMLA.2010.17","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.17","url":null,"abstract":"Most of the traditional text classification methods employ Bag of Words (BOW) approaches relying on the words frequencies existing within the training corpus and the testing documents. Recently, studies have examined using external knowledge to enrich the text representation of documents. Some have focused on using WordNet which suffers from different limitations including the available number of words, synsets and coverage. Other studies used different aspects of Wikipedia instead. Depending on the features being selected and evaluated and the external knowledge being used, a balance between recall, precision, noise reduction and information loss has to be applied. In this paper, we propose a new Centroid-based classification approach relying on Wikipedia to enrich the representation of documents through the use of Wikpedia’s concepts, categories structure, links, and articles text. We extract candidate concepts for each class with the help of Wikipedia and merge them with important features derived directly from the text documents. Different variations of the system were evaluated and the results show improvements in the performance of the system.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"183 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127029623","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 study how to effectively integrate reinforcement learning (RL) and programming languages via adaptation-based programming, where programs can include non-deterministic structures that can be automatically optimized via RL. Prior work has optimized adaptive programs by defining an induced sequential decision process to which standard RL is applied. Here we show that the success of this approach is highly sensitive to the specific program structure, where even seemingly minor program transformations can lead to failure. This sensitivity makes it extremely difficult for a non-RL-expert to write effective adaptive programs. In this paper, we study a more robust learning approach, where the key idea is to leverage information about program structure in order to define a more informative decision process and to improve the SARSA(lambda) RL algorithm. Our empirical results show significant benefits for this approach.
{"title":"Robust Learning for Adaptive Programs by Leveraging Program Structure","authors":"Jervis Pinto, Alan Fern, Tim Bauer, Martin Erwig","doi":"10.1109/ICMLA.2010.150","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.150","url":null,"abstract":"We study how to effectively integrate reinforcement learning (RL) and programming languages via adaptation-based programming, where programs can include non-deterministic structures that can be automatically optimized via RL. Prior work has optimized adaptive programs by defining an induced sequential decision process to which standard RL is applied. Here we show that the success of this approach is highly sensitive to the specific program structure, where even seemingly minor program transformations can lead to failure. This sensitivity makes it extremely difficult for a non-RL-expert to write effective adaptive programs. In this paper, we study a more robust learning approach, where the key idea is to leverage information about program structure in order to define a more informative decision process and to improve the SARSA(lambda) RL algorithm. Our empirical results show significant benefits for this approach.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130660994","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 work presents a new approach based on support vector regression to deal with incomplete input (unseen) data and compares it to other existing techniques. The empirical analysis has been done over 18 real data sets and using five different classifiers, with the aim of foreseeing which technique can be deemed as more suitable for each classifier. Also, this study tries to devise how the relevance of the missing attribute affects the performance of each pair (handling algorithm, classifier). Experimental results demonstrate that no technique is absolutely better than the others for all classifiers. However, combining the proposed strategy with the nearest neighbor classifier appears as the best choice to face the problem of missing attribute values in the input data.
{"title":"A Comparison of Techniques for Handling Incomplete Input Data with a Focus on Attribute Relevance Influence","authors":"M. Millán-Giraldo, J. S. Sánchez, V. Traver","doi":"10.1109/ICMLA.2010.126","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.126","url":null,"abstract":"This work presents a new approach based on support vector regression to deal with incomplete input (unseen) data and compares it to other existing techniques. The empirical analysis has been done over 18 real data sets and using five different classifiers, with the aim of foreseeing which technique can be deemed as more suitable for each classifier. Also, this study tries to devise how the relevance of the missing attribute affects the performance of each pair (handling algorithm, classifier). Experimental results demonstrate that no technique is absolutely better than the others for all classifiers. However, combining the proposed strategy with the nearest neighbor classifier appears as the best choice to face the problem of missing attribute values in the input data.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125122441","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}
Noise filtering is a commonly-used methodology to improve the performance of learners built using low-quality data. A common type of noise filtering is a data preprocessing technique called classification filtering. In classification filtering, a classifier is built and evaluated on the training dataset (typically using cross-validation) and any misclassified instances are considered noisy. The strategies employed with classification filters are not ideal, particularly when learning from class-imbalanced data. To address this deficiency, we propose an alternative method for classification filtering called the threshold-adjusted classification filter. This methodology is compared with the standard classification filter, and the results clearly demonstrate the efficacy of our technique.
{"title":"A Novel Noise Filtering Algorithm for Imbalanced Data","authors":"J. V. Hulse, T. Khoshgoftaar, Amri Napolitano","doi":"10.1109/ICMLA.2010.9","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.9","url":null,"abstract":"Noise filtering is a commonly-used methodology to improve the performance of learners built using low-quality data. A common type of noise filtering is a data preprocessing technique called classification filtering. In classification filtering, a classifier is built and evaluated on the training dataset (typically using cross-validation) and any misclassified instances are considered noisy. The strategies employed with classification filters are not ideal, particularly when learning from class-imbalanced data. To address this deficiency, we propose an alternative method for classification filtering called the threshold-adjusted classification filter. This methodology is compared with the standard classification filter, and the results clearly demonstrate the efficacy of our technique.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134104697","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 goal of this research is to find how dependencies affect the capability of several feature selection approaches to extract of the relevant features for a classification purpose. The hypothesis is that more dependencies and higher level dependencies mean more complexity for the task. Some experiments are used to intend to discover some limitations of several feature selection approaches by altering the degree of dependency of the test datasets. A new method has been proposed, which uses a pair of pre-designed Bayesian Networks to generate the test datasets with an easy tuning level of complexity for feature selection test. Relief, CFS, NB-GA, NB-BOA, SVM-GA, SVM-BOA and SVM-mBOA are the filter or wrapper model feature selection approaches which are used and evaluated in the experiments. For these approaches, higher level of dependency among the relevant features greatly affect the capability to find the relevant features for classification. For Relief, SVM-BOA and SVM-mBOA, if the dependencies among the irrelevant features are altered, the performance of them changes as well. Moreover, a multi-objective optimization method is used to keep the diversity of the populations in each generation of the BOA search algorithm improving the overall quality of solutions in our experiments.
{"title":"How Dependencies Affect the Capability of Several Feature Selection Approaches to Extract the Key Features","authors":"Qin Yang, R. Gras","doi":"10.1109/ICMLA.2010.26","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.26","url":null,"abstract":"The goal of this research is to find how dependencies affect the capability of several feature selection approaches to extract of the relevant features for a classification purpose. The hypothesis is that more dependencies and higher level dependencies mean more complexity for the task. Some experiments are used to intend to discover some limitations of several feature selection approaches by altering the degree of dependency of the test datasets. A new method has been proposed, which uses a pair of pre-designed Bayesian Networks to generate the test datasets with an easy tuning level of complexity for feature selection test. Relief, CFS, NB-GA, NB-BOA, SVM-GA, SVM-BOA and SVM-mBOA are the filter or wrapper model feature selection approaches which are used and evaluated in the experiments. For these approaches, higher level of dependency among the relevant features greatly affect the capability to find the relevant features for classification. For Relief, SVM-BOA and SVM-mBOA, if the dependencies among the irrelevant features are altered, the performance of them changes as well. Moreover, a multi-objective optimization method is used to keep the diversity of the populations in each generation of the BOA search algorithm improving the overall quality of solutions in our experiments.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132147846","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}
To construct biologically interpretable features and facilitate Muscular Dystrophy (MD) sub-types classification, we propose a novel integrative scheme utilizing PPI network, functional gene sets information, and mRNA profiling. The workflow of the proposed scheme includes three major steps: First, by combining protein–protein interaction network structure and gene co-expression relationship into new distance metric, we apply affinity propagation clustering to build gene sub-networks. Secondly, we further incorporate functional gene sets knowledge to complement the physical interaction information. Finally, based on constructed sub-network and gene set features, we apply multi-class support vector machine (MSVM) for MD sub-type classification, and highlight the biomarkers contributing to the sub-type prediction. The experimental results show that our scheme could construct sub-networks that are more relevant to MD than those constructed by conventional approach. Furthermore, our integrative strategy substantially improved the prediction accuracy, especially for those hard-to-classify sub-types.
{"title":"Computational Analysis of Muscular Dystrophy Sub-types Using a Novel Integrative Scheme","authors":"Chen Wang, S. S. Ha, Y. Wang, J. Xuan, E. Hoffman","doi":"10.1109/ICMLA.2010.49","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.49","url":null,"abstract":"To construct biologically interpretable features and facilitate Muscular Dystrophy (MD) sub-types classification, we propose a novel integrative scheme utilizing PPI network, functional gene sets information, and mRNA profiling. The workflow of the proposed scheme includes three major steps: First, by combining protein–protein interaction network structure and gene co-expression relationship into new distance metric, we apply affinity propagation clustering to build gene sub-networks. Secondly, we further incorporate functional gene sets knowledge to complement the physical interaction information. Finally, based on constructed sub-network and gene set features, we apply multi-class support vector machine (MSVM) for MD sub-type classification, and highlight the biomarkers contributing to the sub-type prediction. The experimental results show that our scheme could construct sub-networks that are more relevant to MD than those constructed by conventional approach. Furthermore, our integrative strategy substantially improved the prediction accuracy, especially for those hard-to-classify sub-types.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130492798","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}
Manifold learning and dimensional reduction methods provide a low dimensional embedding for a collection of training samples. These methods are based on the eigenvalue decomposition of the kernel matrix formed using the training samples. In [2] the embedding is extended to new test samples using the Nystrom approximation method. This paper addresses the pre-image problem for these methods, which is to find the mapping back from the embedding space to the input space for new test points. The relationship of these learning methods to kernel principal component analysis [6] and the connection of the out-of-sample problem to the pre-image problem [1] is used to provide the pre-image.
{"title":"Pre-image Problem in Manifold Learning and Dimensional Reduction Methods","authors":"Omar Arif, P. Vela, W. Daley","doi":"10.1109/ICMLA.2010.146","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.146","url":null,"abstract":"Manifold learning and dimensional reduction methods provide a low dimensional embedding for a collection of training samples. These methods are based on the eigenvalue decomposition of the kernel matrix formed using the training samples. In [2] the embedding is extended to new test samples using the Nystrom approximation method. This paper addresses the pre-image problem for these methods, which is to find the mapping back from the embedding space to the input space for new test points. The relationship of these learning methods to kernel principal component analysis [6] and the connection of the out-of-sample problem to the pre-image problem [1] is used to provide the pre-image.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124039365","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}
Everyone is familiar with the scenario, people demand or assign tasks to robots, and robots execute the tasks to serve people. We call such a model Serve-on-Demand. With the advancement of pervasive computing, machine learning and artificial intelligence, the robot service of the next generation will inevitably turn to actively and exactly meet people’s needs, even without explicit demand. We call it Serve-on-Need. It requires the robots to comprehend the intentions and preferences of people exactly. In this paper, we model the human-computer interaction for Serve-on-Need as a repeated stochastic Bayesian game. We solve the stochastic Bayesian game by an equilibrium analysis and rational learning. We present the service of a coffee robot to illustrate such an approach.
{"title":"From Serve-on-Demand to Serve-on-Need: A Game Theoretic Approach","authors":"Yong Lin, F. Makedon","doi":"10.1109/ICMLA.2010.12","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.12","url":null,"abstract":"Everyone is familiar with the scenario, people demand or assign tasks to robots, and robots execute the tasks to serve people. We call such a model Serve-on-Demand. With the advancement of pervasive computing, machine learning and artificial intelligence, the robot service of the next generation will inevitably turn to actively and exactly meet people’s needs, even without explicit demand. We call it Serve-on-Need. It requires the robots to comprehend the intentions and preferences of people exactly. In this paper, we model the human-computer interaction for Serve-on-Need as a repeated stochastic Bayesian game. We solve the stochastic Bayesian game by an equilibrium analysis and rational learning. We present the service of a coffee robot to illustrate such an approach.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124033278","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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