2010 Ninth International Conference on Machine Learning and Applications最新文献_第8页

Improved Fine-Grained Component-Conditional Class Labeling with Active Learning 基于主动学习的改进细粒度组件条件类标记

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.8

David J. Miller, Chu-Fang Lin, G. Kesidis, Christopher M. Collins

{"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}

引用次数: 0

Hardware Implementation of a Real-Time Neural Network Controller Set for Reactive Power Compensation Systems 无功补偿系统实时神经网络控制器的硬件实现

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.107

R. Bayindir, Alper Gorgun

引用次数: 5

Centroid-based Classification Enhanced with Wikipedia 基于质心的分类与维基百科增强

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.17

Abdullah Bawakid, M. Oussalah

{"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}

引用次数: 3

Robust Learning for Adaptive Programs by Leveraging Program Structure 利用程序结构的自适应程序鲁棒学习

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.150

Jervis Pinto, Alan Fern, Tim Bauer, Martin Erwig

引用次数: 7

A Comparison of Techniques for Handling Incomplete Input Data with a Focus on Attribute Relevance Influence 以属性关联影响为重点的不完全输入数据处理技术比较

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.126

M. Millán-Giraldo, J. S. Sánchez, V. Traver

引用次数: 3

A Novel Noise Filtering Algorithm for Imbalanced Data 一种新的不平衡数据噪声滤波算法

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.9

J. V. Hulse, T. Khoshgoftaar, Amri Napolitano

引用次数: 17

How Dependencies Affect the Capability of Several Feature Selection Approaches to Extract the Key Features 依赖关系如何影响几种特征选择方法提取关键特征的能力

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.26

Qin Yang, R. Gras

{"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}

引用次数: 3

Computational Analysis of Muscular Dystrophy Sub-types Using a Novel Integrative Scheme 用一种新的综合方案计算分析肌肉萎缩症亚型

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.49

Chen Wang, S. S. Ha, Y. Wang, J. Xuan, E. Hoffman

{"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}

引用次数: 5

Boosting Multi-Task Weak Learners with Applications to Textual and Social Data 基于文本和社会数据的多任务弱学习者应用

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.61

J. Faddoul, Boris Chidlovskii, Fabien Torre, Rémi Gilleron

引用次数: 15

Plant Species Classification Using a 3D LIDAR Sensor and Machine Learning 利用3D激光雷达传感器和机器学习进行植物物种分类

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.57

Ulrich Weiss, P. Biber, Stefan Laible, K. Bohlmann, A. Zell

{"title":"Plant Species Classification Using a 3D LIDAR Sensor and Machine Learning","authors":"Ulrich Weiss, P. Biber, Stefan Laible, K. Bohlmann, A. Zell","doi":"10.1109/ICMLA.2010.57","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.57","url":null,"abstract":"In the domain of agricultural robotics, one major application is crop scouting, e.g., for the task of weed control. For this task a key enabler is a robust detection and classification of the plant and species. Automatically distinguishing between plant species is a challenging task, because some species look very similar. It is also difficult to translate the symbolic high level description of the appearances and the differences between the plants used by humans, into a formal, computer understandable form. Also it is not possible to reliably detect structures, like leaves and branches in 3D data provided by our sensor. One approach to solve this problem is to learn how to classify the species by using a set of example plants and machine learning methods. In this paper we are introducing a method for distinguishing plant species using a 3D LIDAR sensor and supervised learning. For that we have developed a set of size and rotation invariant features and evaluated experimentally which are the most descriptive ones. Besides these features we have also compared different learning methods using the toolbox Weka. It turned out that the best methods for our application are simple logistic regression functions, support vector machines and neural networks. In our experiments we used six different plant species, typically available at common nurseries, and about 20 examples of each species. In the laboratory we were able to identify over 98% of these plants correctly.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"14 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":"115581041","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}

引用次数: 51