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McCallum, Andrew; Nigam, Kamal; Rennie, Jason D. M.; Seymore, Kristie

doi:10.1023/a:1009953814988

Cited by 803 publications

(65 citation statements)

References 25 publications

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“…All results will be converted back into a multi‐label statement after single‐label classifications. There are a series of state‐of‐the‐art conversion methods, such as support vector machines (SVMs), Naive Bayes, and k Nearest Neighbor methods. We will consider more single‐label classifiers and multi‐label classifiers with our dataset

D

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Section: Methodsmentioning

confidence: 99%

HPSLPred: An Ensemble Multi‐Label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source

2017

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Protein subcellular localization prediction is an important and challenging problem. The traditional biology experiments are expensive and time-consuming, so more and more research interests tend to a series of machine learning approaches for predicting protein subcellular location. There are two main difficult problems among the existing state-of-the-art methods. First, most of the existing techniques are designed to deal with the multi-class but not the multi-label classification, which ignores the connection between the multiple labels. In reality, multiple location proteins implicate that there are vital and unique biological significances worthy of special focus, which cannot be ignored. Second, the techniques for handling imbalanced data in multi-label classification problem is significant but less. For solving the two issues, we have developed an ensemble multi-label classifier called HPSLPred which can be applied for the multi-label classification with imbalanced protein source. For the conveniences of users, a user-friendly webserver for HPSLPred was established at http://server.malab.cn/HPSLPred.

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D

.…”

Section: Methodsmentioning

confidence: 99%

HPSLPred: An Ensemble Multi‐Label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source

2017

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“…4 This model was also named as Partial CRF (Carlson et al, 2009) and EM Marginal CRF (Greenberg et al, 2018). variable CRF (Quattoni et al, 2005) on citation parsing (McCallum et al, 2000). This model had also been used in part-of-speeching tagging and segmentation task with incomplete annotations (Tsuboi et al, 2008;Liu et al, 2014;Yang and Vozila, 2014).…”

Section: Related Workmentioning

confidence: 99%

Better Modeling of Incomplete Annotations for Named Entity Recognition

Jie¹,

Xie²,

Lu³

et al. 2019

Proceedings of the 2019 Conference of the North

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Supervised approaches to named entity recognition (NER) are largely developed based on the assumption that the training data is fully annotated with named entity information. However, in practice, annotated data can often be imperfect with one typical issue being the training data may contain incomplete annotations. We highlight several pitfalls associated with learning under such a setup in the context of NER and identify limitations associated with existing approaches, proposing a novel yet easy-to-implement approach for recognizing named entities with incomplete data annotations. We demonstrate the effectiveness of our approach through extensive experiments. 1

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“…We experimented with 6 real networks from 3 domains to examine the performance of the proposed UNBC methods. The datasets are Cora [15], [31], [32], Imdb [10], [33], [34], and four computer science departments' web pages in the WebKB project [15], [34]- [36]. The WebKB datasets include Texas, Cornell, Wisconsin, and Washington.…”

Section: B Datamentioning

confidence: 99%

Second-Order Markov Assumption Based Bayes Classifier for Networked Data With Heterophily

et al. 2019

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The classification of networked data is an interesting and challenging problem. Most traditional relational classifiers that are based on the principle of homophily have an unsatisfactory classification performance in networks with heterophily. This is because these methods treat inhomogeneous networks homogeneously. A progression of a network-only Bayes-classifier-based second-order Markov assumption is proposed for heterophilous networks in this paper to address this problem. First, we estimate the class distribution of an unlabeled node according to the class distribution of its neighbors' neighbors. In this process, we perform this computation on the known and unknown neighbors separately. Second, we combine the two parts using multinomial naïve Bayesian classification. Meanwhile, we pair a relaxation labeling collective inference method (which imports simulated annealing) with this new method to update the class distributions at each iteration. Comparisons of the experimental results demonstrate that the proposed method performs better when the networks are heterophilous. INDEX TERMS Artificial intelligence, data mining, heterophilous networks, machine learning, networked data classification, relational classifier.

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Untitled

Cited by 803 publications

References 25 publications

HPSLPred: An Ensemble Multi‐Label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source

HPSLPred: An Ensemble Multi‐Label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source

Better Modeling of Incomplete Annotations for Named Entity Recognition

Second-Order Markov Assumption Based Bayes Classifier for Networked Data With Heterophily

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