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46
Effective Multi-Label Active Learning for Text Classification
, 2009
"... Labeling text data is quite time-consuming but essential for automatic text classification. Especially, manually creating multiple labels for each document may become impractical when a very large amount of data is needed for training multi-label text classifiers. To minimize the human-labeling effo ..."
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Cited by 32 (0 self)
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Labeling text data is quite time-consuming but essential for automatic text classification. Especially, manually creating multiple labels for each document may become impractical when a very large amount of data is needed for training multi-label text classifiers. To minimize the human-labeling efforts, we propose a novel multi-label active learning ap-proach which can reduce the required labeled data with-out sacrificing the classification accuracy. Traditional active learning algorithms can only handle single-label problems, that is, each data is restricted to have one label. Our ap-proach takes into account the multi-label information, and aims to label data which can optimize the expected loss re-duction. Specifically, the model loss is approximated by the size of version space, and we optimize the reduction rate of the size of version space with Support Vector Machines (SVM). Furthermore, we design an effective method to pre-dict possible labels for each unlabeled data point, and ap-proximate the expected loss by summing up losses on all labels according to the most confident result of label pre-diction. Experiments on seven real-world data sets (all are publicly available) demonstrate that our approach can ob-tain promising classification result with much fewer labeled data than state-of-the-art methods.
Hyperspectral Image Segmentation Using a New Bayesian Approach with Active Learning
"... This paper introduces a new supervised Bayesian approach to hyperspectral image segmentation with active learning, which consists of two main steps: (a) learning, for each class label, the posterior probability distributions using a multinomial logistic regression model; (b) segmenting the hyperspec ..."
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Cited by 18 (10 self)
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This paper introduces a new supervised Bayesian approach to hyperspectral image segmentation with active learning, which consists of two main steps: (a) learning, for each class label, the posterior probability distributions using a multinomial logistic regression model; (b) segmenting the hyperspectral image based on the posterior probability distribution learned in step (a) and on a multi-level logistic prior which encodes the spatial information. The multinomial logistic regressors are learned by using the recently introduced logistic regression via splitting and augmented Lagrangian (LORSAL) algorithm. The maximum a posteriori segmentation is efficiently computed by the α-Expansion min-cut based integer optimization algorithm. Aiming at reducing the costs of acquiring large training sets, active learning is performed using a mutual information based criterion. The state-of-the-art performance of the proposed approach is illustrated using both simulated and real hyperspectral data sets in a number of experimental comparisons with recently introduced hyperspectral image classification methods. Index Terms Hyperspectral image segmentation, sparse multinomial logistic regression, ill-posed problems, graph cuts, integer optimization, mutual information, active learning. I.
Spectral–spatial classification of hyperspectral data using loopy belief propagation and active learning
- IEEE Trans. Geosci. Remote Sens
"... Abstract—In this paper, we propose a new framework for spectral–spatial classification of hyperspectral image data. The proposed approach serves as an engine in the context of which active learning algorithms can exploit both spatial and spectral information simultaneously. An important contribution ..."
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Cited by 11 (2 self)
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Abstract—In this paper, we propose a new framework for spectral–spatial classification of hyperspectral image data. The proposed approach serves as an engine in the context of which active learning algorithms can exploit both spatial and spectral information simultaneously. An important contribution of our paper is the fact that we exploit the marginal probability distribution which uses the whole information in the hyperspectral data. We learn such distributions from both the spectral and spatial information contained in the original hyperspectral data using loopy belief propagation. The adopted probabilistic model is a discriminative random field in which the association potential is a multinomial logistic regression classifier and the interaction potential is a Markov random field multilevel logistic prior. Our experimental results with hyperspectral data sets collected using the National Aeronautics and Space Administration’s Airborne Visible Infrared Imaging Spectrometer and the Reflective Optics System Imaging Spectrometer system indicate that the proposed framework provides state-of-the-art performance when compared to other similar developments. Index Terms—Active learning (AL), discriminative random fields (DRFs), hyperspectral image classification, loopy belief propagation (LBP), Markov random fields (MRFs), spectral– spatial analysis. I.
Activized Learning: Transforming Passive to Active with Improved Label Complexity
"... Active learning methods often achieve improved performance using fewer labels compared to passive learning methods. A variety of practically successful active learning algorithms use a passive learning algorithm as a subroutine, and the essential role of the active component is to construct data set ..."
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Cited by 11 (4 self)
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Active learning methods often achieve improved performance using fewer labels compared to passive learning methods. A variety of practically successful active learning algorithms use a passive learning algorithm as a subroutine, and the essential role of the active component is to construct data sets to feed into the passive subroutine. This general idea is appealing for a variety of reasons, as it may be able
Active Learning from Multiple Noisy Labelers with Varied Costs
- In IEEE 10th International Conference on Data Mining (ICDM
, 2010
"... Abstract—In active learning, where a learning algorithm has to purchase the labels of its training examples, it is often assumed that there is only one labeler available to label examples, and that this labeler is noise-free. In reality, it is possible that there are multiple labelers available (suc ..."
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Cited by 8 (0 self)
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Abstract—In active learning, where a learning algorithm has to purchase the labels of its training examples, it is often assumed that there is only one labeler available to label examples, and that this labeler is noise-free. In reality, it is possible that there are multiple labelers available (such as human labelers in the online annotation tool Amazon Mechanical Turk) and that each such labeler has a different cost and accuracy. We address the active learning problem with multiple labelers where each labeler has a different (known) cost and a different (unknown) accuracy. Our approach uses the idea of adjusted cost, which allows labelers with different costs and accuracies to be directly compared. This allows our algorithm to find low-cost combinations of labelers that result in high-accuracy labelings of instances. Our algorithm further reduces costs by pruning under-performing labelers from the set under consideration, and by halting the process of estimating the accuracy of the labelers as early as it can. We found that our algorithm often outperforms, and is always competitive with, other algorithms in the literature. Keywords-active learning; multiple labelers; noisy labelers; algorithms; adjusted cost I.
Active learning to maximize area under the ROC curve
, 2006
"... In active learning, a machine learning algorithm is given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. The goal of active learning is to judiciously choose which examples in U to have labeled in order to optimize some perfor ..."
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Cited by 8 (1 self)
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In active learning, a machine learning algorithm is given an unlabeled set of examples U, and is allowed to request labels for a relatively small subset of U to use for training. The goal of active learning is to judiciously choose which examples in U to have labeled in order to optimize some performance criterion, e.g. generalization accuracy. ROC (Receiver Operating Characteristic) analysis has attracted high attention in machine learning research in the last few years. ROC curves have been advocated and gradually adopted as an al-ternative to classical machine learning metrics such as misclassification rate. We present several heuristics for active learning designed to optimize area under the ROC curve (AUC) and extensively evaluate them, along with other commonly-used active learning algorithms. One of our algorithms (ESTAUC) was the top performer. When good posterior probability estimates were available, ESTAUC and another of our heuristics (RAR) were by far the best.
Active Learning with Multi-label SVM Classification
- In IJCAI
, 2013
"... Multi-label classification, where each instance is assigned to multiple categories, is a prevalent problem in data analysis. However, annotations of multi-label instances are typically more time-consuming or expensive to obtain than annotations of single-label instances. Though active learning has b ..."
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Cited by 7 (0 self)
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Multi-label classification, where each instance is assigned to multiple categories, is a prevalent problem in data analysis. However, annotations of multi-label instances are typically more time-consuming or expensive to obtain than annotations of single-label instances. Though active learning has been widely studied on reducing labeling ef-fort for single-label problems, current research on multi-label active learning remains in a preliminary state. In this paper, we first propose two novel multi-label active learning strategies, a max-margin prediction uncertainty strategy and a label cardinal-ity inconsistency strategy, and then integrate them into an adaptive framework of multi-label active learning. Our empirical results on multiple multi-label data sets demonstrate the efficacy of the pro-posed active instance selection strategies and the in-tegrated active learning approach. 1
Batch Mode Active Learning with Applications to Text Categorization and Image Retrieval
"... Abstract—Most machine learning tasks in data classification and information retrieval require manually labeled data examples in the training stage. The goal of active learning is to select the most informative examples for manual labeling in these learning tasks. Most of the previous studies in acti ..."
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Cited by 5 (0 self)
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Abstract—Most machine learning tasks in data classification and information retrieval require manually labeled data examples in the training stage. The goal of active learning is to select the most informative examples for manual labeling in these learning tasks. Most of the previous studies in active learning have focused on selecting a single unlabeled example in each iteration. This could be inefficient, since the classification model has to be retrained for every acquired labeled example. It is also inappropriate for the setup of information retrieval tasks where the user’s relevance feedback is often provided for the top K retrieved items. In this paper, we present a framework for batch mode active learning, which selects a number of informative examples for manual labeling in each iteration. The key feature of batch mode active learning is to reduce the redundancy among the selected examples such that each example provides unique information for model updating. To this end, we employ the Fisher information matrix as the measurement of model uncertainty, and choose the set of unlabeled examples that can efficiently reduce the Fisher information of the classification model. We apply our batch mode active learning framework to both text categorization and image retrieval. Promising results show that our algorithms are significantly more effective than the active learning approaches that select unlabeled examples based only on their informativeness for the classification model. Index Terms—Batch mode active learning, logistic regressions, kernel logistic regressions, convex optimization, text categorization, image retrieval. Ç 1
Selective sampling algorithms for cost-sensitive multiclass prediction
"... In this paper, we study the problem of active learning for cost-sensitive multiclass classification. We propose selective sampling algorithms, which process the data in a streaming fashion, querying only a subset of the labels. For these algorithms, we analyze the regret and label complexity when th ..."
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Cited by 4 (1 self)
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In this paper, we study the problem of active learning for cost-sensitive multiclass classification. We propose selective sampling algorithms, which process the data in a streaming fashion, querying only a subset of the labels. For these algorithms, we analyze the regret and label complexity when the labels are generated according to a generalized linear model. We establish that the gains of active learning over passive learning can range from none to exponentially large, based on a natural notion of margin. We also present a safety guarantee to guard against model mismatch. Numerical simulations show that our algorithms indeed obtain a low regret with a small number of queries. 1.
EXPLORATION-BASED ACTIVE MACHINE LEARNING
, 2005
"... Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert). Many conventional active learning algorithms focus on refining the decision boundary, at the expense of exploring new r ..."
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Cited by 4 (0 self)
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Active machine learning algorithms are used when large numbers of unlabeled examples are available and getting labels for them is costly (e.g. requiring consulting a human expert). Many conventional active learning algorithms focus on refining the decision boundary, at the expense of exploring new regions that the current hypothesis misclassifies. We propose a new active learning algorithm that balances such explo-ration with refining of the decision boundary by dynamically adjusting the probability to explore at each step. Our experimental results demonstrate improved performance on data sets that require extensive exploration while remaining competitive on data