Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction

Cohen, Ira L.; Cozman, Fábio Gagliardi; Sebe, Nicu; Cirelo, M.C.; Huang, Thomas S.

doi:10.1109/tpami.2004.127

Cited by 232 publications

(145 citation statements)

References 43 publications

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“…are not able to match the underlying generative structure [10]. This phenomenon occurs in both evaluation measures.…”

Section: 22mentioning

confidence: 94%

“…As stated in [10], if the correct structure of the real distribution of the data is obtained, unlabelled data improve the classifier, otherwise, unlabelled data can actually degrade performance. For this reason, it seems more appropriate to perform a structural search in order to find the real model.…”

Section: Learning Multi-dimensional Bayesian Network Classifiers In Tmentioning

confidence: 99%

“…As happens in the uni-dimensional framework [10], when using the real structure to semisupervisely learnt multi-dimensional classifiers, the unlabelled data always helps. 2.…”

Section: Artificial Experimentationmentioning

confidence: 99%

“…This characteristic favours the learning scheme provided by the MDnB algorithm. Moreover, this is crucial in the success obtained in the semi-supervised framework due to the fact that it matches the actual underlying domain structure [10].…”

Section: 22mentioning

confidence: 99%

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Approaching Sentiment Analysis by using semi-supervised learning of multi-dimensional classifiers

Ortigosa-Hernández

Rodríguez

Alzate³

et al. 2012

Neurocomputing

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Sentiment Analysis is defined as the computational study of opinions, sentiments and emotions expressed in text. Within this broad field, most of the work has been focused on either Sentiment Polarity classification, where a text is classified as having positive or negative sentiment, or Subjectivity classification, in which a text is classified as being subjective or objective. However, in this paper, we consider instead a real-world problem in which the attitude of the author is characterised by three different (but related) target variables: Subjectivity, Sentiment Polarity, Will to Influence, unlike the two previously stated problems, where there is only a single variable to be predicted. For that reason, the (uni-dimensional) common approaches used in this area yield suboptimal solutions to this problem. In order to bridge this gap, we propose, for the first time, the use of the novel multi-dimensional classification paradigm in the Sentiment Analysis domain. This methodology is able to join the different target variables in the same classification task so as to take advantage of the potential statistical relations between them. In addition, and in order to take advantage of the huge amount of unlabelled information available nowadays in this context, we propose the extension of the multi-dimensional classification framework to the semi-supervised domain. Experimental results for this problem show that our semi-supervised multi-dimensional approach outperforms the most common Sentiment Analysis approaches, concluding that our approach is beneficial to improve the recognition rates for this problem, and in extension, could be considered to solve future Sentiment Analysis problems.

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“…are not able to match the underlying generative structure [10]. This phenomenon occurs in both evaluation measures.…”

Section: 22mentioning

confidence: 94%

Section: Learning Multi-dimensional Bayesian Network Classifiers In Tmentioning

confidence: 99%

“…As happens in the uni-dimensional framework [10], when using the real structure to semisupervisely learnt multi-dimensional classifiers, the unlabelled data always helps. 2.…”

Section: Artificial Experimentationmentioning

confidence: 99%

Section: 22mentioning

confidence: 99%

See 2 more Smart Citations

Approaching Sentiment Analysis by using semi-supervised learning of multi-dimensional classifiers

Ortigosa-Hernández

Rodríguez

Alzate³

et al. 2012

Neurocomputing

View full text Add to dashboard Cite

show abstract

“…Some studies concluded that significant improvements in classification performance can be achieved when unlabeled examples are used, while others have indicated otherwise [4][10] [12] [25]. Blum and Mitchell [4] and Cozman et al [10] suggested that unlabeled data can help to reduce variance of the estimator as long as the modeling assumptions match the ground truth data. Otherwise, unlabeled data may either improve or degrade the classification performance, depending on the complexity of the classifier compared to the training set size [12].…”

Section: Related Workmentioning

confidence: 99%

Semi-supervised learning with data calibration for long-term time series forecasting

Cheng

Tan

2008

Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

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Many time series prediction methods have focused on single step or short term prediction problems due to the inherent difficulty in controlling the propagation of errors from one prediction step to the next step. Yet, there is a broad range of applications such as climate impact assessments and urban growth planning that require long term forecasting capabilities for strategic decision making. Training an accurate model that produces reliable long term predictions would require an extensive amount of historical data, which are either unavailable or expensive to acquire. For some of these domains, there are alternative ways to generate potential scenarios for the future using computer-driven simulation models, such as global climate and traffic demand models. However, the data generated by these models are currently utilized in a supervised learning setting, where a predictive model trained on past observations is used to estimate the future values. In this paper, we present a semisupervised learning framework for long-term time series forecasting based on Hidden Markov Model Regression. A covariance alignment method is also developed to deal with the issue of inconsistencies between historical and model simulation data. We evaluated our approach on data sets from a variety of domains, including climate modeling. Our experimental results demonstrate the efficacy of the approach compared to other supervised learning methods for longterm time series forecasting.

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Artificial immune system for illicit content identification in social media

Yang

Kiang

Chen

et al. 2011

J. Am. Soc. Inf. Sci.

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Social media is frequently used as a platform for the exchange of information and opinions as well as propaganda dissemination. But online content can be misused for the distribution of illicit information, such as violent postings in web forums. Illicit content is highly distributed in social media, while non-illicit content is unspecific and topically diverse. It is costly and time consuming to label a large amount of illicit content (positive examples) and non-illicit content (negative examples) to train classification systems. Nevertheless, it is relatively easy to obtain large volumes of unlabeled content in social media. In this article, an artificial immune systembased technique is presented to address the difficulties in the illicit content identification in social media. Inspired by the positive selection principle in the immune system, we designed a novel labeling heuristic based on partially supervised learning to extract high-quality positive and negative examples from unlabeled datasets. The empirical evaluation results from two large hate group web forums suggest that our proposed approach generally outperforms the benchmark techniques and exhibits more stable performance.

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Semisupervised learning of classifiers: theory, algorithms, and their application to human-computer interaction

Cited by 232 publications

References 43 publications

Approaching Sentiment Analysis by using semi-supervised learning of multi-dimensional classifiers

Approaching Sentiment Analysis by using semi-supervised learning of multi-dimensional classifiers

Semi-supervised learning with data calibration for long-term time series forecasting

Artificial immune system for illicit content identification in social media

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