Recent years have witnessed the quick progress of the hyperspectral images (HSI) classification. Most of existing studies either heavily rely on the expensive label information using the supervised learning or can hardly exploit the discriminative information borrowed from related domains. To address this issues, in this paper we show a novel framework addressing HSI classification based on the domain adaptation (DA) with active learning (AL). The main idea of our method is to retrain the multi-kernel classifier by utilizing the available labeled samples from source domain, and adding minimum number of the most informative samples with active queries in the target domain. The proposed method adaptively combines multiple kernels, forming a DA classifier that minimizes the bias between the source and target domains. Further equipped with the nested actively updating process, it sequentially expands the training set and gradually converges to a satisfying level of classification performance. We study this active adaptation framework with the Margin Sampling (MS) strategy in the HSI classification task. Our experimental results on two popular HSI datasets demonstrate its effectiveness.
Since the labeled information in target domain can directly and notably improve the classification performance, it is obviously helpful that we can exploit a small number of labeled data to boost the classification performance, which at the same time only brings a quite limited additional cost for the labeled data collection. The active learning (AL) strategies have been widely studied in the literature to tackle such a challenging task in recent years @cite_40 @cite_12 @cite_46 @cite_31 , with the aim to exploit the information available from unlabeled data and to enrich the labeled data. In AL process, labelling originally unlabeled data is usually completed by a user according to an specific informative measure.
@cite_46 @cite_40 @cite_31 @cite_12
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Active learning (AL) consists of asking human annotators to annotate automatically selected data that are assumed to bring the most benefit in the creation of a classifier. AL allows to learn accurate systems with much less annotated data than what is required by pure supervised learning algorithms, hence limiting the tedious effort of annotating a large collection of data. We experimentally investigate the behavior of several AL strategies for sequence labeling tasks (in a partially-labeled scenario) tailored on Partially-Labeled Conditional Random Fields, on four sequence labeling tasks: phrase chunking, part-of-speech tagging, named-entity recognition, and bioentity recognition. Active learning (AL) is an increasingly popular strategy for mitigating the amount of labeled data required to train classifiers, thereby reducing annotator effort. We describe a real-world, deployed application of AL to the problem of biomedical citation screening for systematic reviews at the Tufts Medical Center's Evidence-based Practice Center. We propose a novel active learning strategy that exploits a priori domain knowledge provided by the expert (specifically, labeled features)and extend this model via a Linear Programming algorithm for situations where the expert can provide ranked labeled features. Our methods outperform existing AL strategies on three real-world systematic review datasets. We argue that evaluation must be specific to the scenario under consideration. To this end, we propose a new evaluation framework for finite-pool scenarios, wherein the primary aim is to label a fixed set of examples rather than to simply induce a good predictive model. We use a method from medical decision theory for eliciting the relative costs of false positives and false negatives from the domain expert, constructing a utility measure of classification performance that integrates the expert preferences. Our findings suggest that the expert can, and should, provide more information than instance labels alone. In addition to achieving strong empirical results on the citation screening problem, this work outlines many important steps for moving away from simulated active learning and toward deploying AL for real-world applications. The active learning (AL) framework is an increasingly popular strategy for reducing the amount of human labeling effort required to induce a predictive model. Most work in AL has assumed that a single, infallible oracle provides labels requested by the learner at a fixed cost. However, real-world applications suitable for AL often include multiple domain experts who provide labels of varying cost and quality. We explore this multiple expert active learning (MEAL) scenario and develop a novel algorithm for instance allocation that exploits the meta-cognitive abilities of novice (cheap) experts in order to make the best use of the experienced (expensive) annotators. We demonstrate that this strategy outperforms strong baseline approaches to MEAL on both a sentiment analysis dataset and two datasets from our motivating application of biomedical citation screening. Furthermore, we provide evidence that novice labelers are often aware of which instances they are likely to mislabel. While Active Learning (AL) has already been shown to markedly reduce the annotation efforts for many sequence labeling tasks compared to random selection, AL remains unconcerned about the internal structure of the selected sequences (typically, sentences). We propose a semi-supervised AL approach for sequence labeling where only highly uncertain subsequences are presented to human annotators, while all others in the selected sequences are automatically labeled. For the task of entity recognition, our experiments reveal that this approach reduces annotation efforts in terms of manually labeled tokens by up to 60 compared to the standard, fully supervised AL scheme.