The Experts below are selected from a list of 81 Experts worldwide ranked by ideXlab platform
Haibo He - One of the best experts on this subject based on the ideXlab platform.
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DCPE co-training: Co-training based on Diversity of class probability estimation
The 2010 International Joint Conference on Neural Networks (IJCNN), 2010Co-Authors: Jin Xu, Haibo HeAbstract:Co-training is a semi-supervised learning technique used to recover the unlabeled data based on two base learners. The normal co-training approaches use the most confidently recovered unlabeled data to augment the training data. In this paper, we investigate the co-training approaches with a focus on the Diversity Issue and propose the Diversity of class probability estimation (DCPE) co-training approach. The key idea of the DCPE co-training method is to use DCPE between two base learners to choose the recovered unlabeled data. The results are compared with classic co-training, tri-training and self training methods. Our experimental study based on the UCI benchmark data sets shows that the DCPE co-training is robust and efficient in the classification.
Jin Xu - One of the best experts on this subject based on the ideXlab platform.
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DCPE co-training: Co-training based on Diversity of class probability estimation
The 2010 International Joint Conference on Neural Networks (IJCNN), 2010Co-Authors: Jin Xu, Haibo HeAbstract:Co-training is a semi-supervised learning technique used to recover the unlabeled data based on two base learners. The normal co-training approaches use the most confidently recovered unlabeled data to augment the training data. In this paper, we investigate the co-training approaches with a focus on the Diversity Issue and propose the Diversity of class probability estimation (DCPE) co-training approach. The key idea of the DCPE co-training method is to use DCPE between two base learners to choose the recovered unlabeled data. The results are compared with classic co-training, tri-training and self training methods. Our experimental study based on the UCI benchmark data sets shows that the DCPE co-training is robust and efficient in the classification.
Mengjie Zhang - One of the best experts on this subject based on the ideXlab platform.
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Genetic Programming With a New Representation to Automatically Learn Features and Evolve Ensembles for Image Classification
IEEE Transactions on Cybernetics, 1Co-Authors: Ying Bi, Mengjie ZhangAbstract:Image classification is a popular task in machine learning and computer vision, but it is very challenging due to high variation crossing images. Using ensemble methods for solving image classification can achieve higher classification performance than using a single classification algorithm. However, to obtain a good ensemble, the component (base) classifiers in an ensemble should be accurate and diverse. To solve image classification effectively, feature extraction is necessary to transform raw pixels into high-level informative features. However, this process often requires domain knowledge. This article proposes an evolutionary approach based on genetic programming to automatically and simultaneously learn informative features and evolve effective ensembles for image classification. The new approach takes raw images as inputs and returns predictions of class labels based on the evolved classifiers. To achieve this, a new individual representation, a new function set, and a new terminal set are developed to allow the new approach to effectively find the best solution. More important, the solutions of the new approach can extract informative features from raw images and can automatically address the Diversity Issue of the ensembles. In addition, the new approach can automatically select and optimize the parameters for the classification algorithms in the ensemble. The performance of the new approach is examined on 13 different image classification datasets of varying difficulty and compared with a large number of effective methods. The results show that the new approach achieves better classification accuracy on most datasets than the competitive methods. Further analysis demonstrates that the new approach can evolve solutions with high accuracy and Diversity.
Han-chieh Chao - One of the best experts on this subject based on the ideXlab platform.
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Indoor smartphone localization via fingerprint crowdsourcing: challenges and approaches
IEEE Wireless Communications, 2016Co-Authors: Bang Wang, Qiuyun Chen, Laurence T. Yang, Han-chieh ChaoAbstract:Nowadays, smartphones have become indispensable to everyone, with more and more built-in location-based applications to enrich our daily life. In the last decade, fingerprinting based on RSS has become a research focus in indoor localization, due to its minimum hardware requirement and satisfiable positioning accuracy. However, its time-consuming and labor-intensive site survey is a big hurdle for practical deployments. Fingerprint crowdsourcing has recently been promoted to relieve the burden of site survey by allowing common users to contribute to fingerprint collection in a participatory sensing manner. For its promising commitment, new challenges arise to practice fingerprint crowdsourcing. This article first identifies two main challenging Issues, fingerprint annotation and device Diversity, and then reviews the state of the art of fingerprint crowdsourcing-based indoor localization systems, comparing their approaches to cope with the two challenges. We then propose a new indoor subarea localization scheme via fingerprint crowdsourcing, clustering, and matching, which first constructs subarea fingerprints from crowdsourced RSS measurements and relates them to indoor layouts. We also propose a new online localization algorithm to deal with the device Diversity Issue. Our experiment results show that in a typical indoor scenario, the proposed scheme can achieve a 95 percent hit rate to correctly locate a smartphone in its subarea.
Ying Bi - One of the best experts on this subject based on the ideXlab platform.
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Genetic Programming With a New Representation to Automatically Learn Features and Evolve Ensembles for Image Classification
IEEE Transactions on Cybernetics, 1Co-Authors: Ying Bi, Mengjie ZhangAbstract:Image classification is a popular task in machine learning and computer vision, but it is very challenging due to high variation crossing images. Using ensemble methods for solving image classification can achieve higher classification performance than using a single classification algorithm. However, to obtain a good ensemble, the component (base) classifiers in an ensemble should be accurate and diverse. To solve image classification effectively, feature extraction is necessary to transform raw pixels into high-level informative features. However, this process often requires domain knowledge. This article proposes an evolutionary approach based on genetic programming to automatically and simultaneously learn informative features and evolve effective ensembles for image classification. The new approach takes raw images as inputs and returns predictions of class labels based on the evolved classifiers. To achieve this, a new individual representation, a new function set, and a new terminal set are developed to allow the new approach to effectively find the best solution. More important, the solutions of the new approach can extract informative features from raw images and can automatically address the Diversity Issue of the ensembles. In addition, the new approach can automatically select and optimize the parameters for the classification algorithms in the ensemble. The performance of the new approach is examined on 13 different image classification datasets of varying difficulty and compared with a large number of effective methods. The results show that the new approach achieves better classification accuracy on most datasets than the competitive methods. Further analysis demonstrates that the new approach can evolve solutions with high accuracy and Diversity.