Building Classifiers with GMDH for Health Social Networks (DB AskaPatient)

Akhtyamova, Liliya; Alexandrov, Mikhail; Cardiff, John; Koshulko, Olexiy

doi:10.1109/stc-csit.2018.8526655

Cited by 3 publications

(3 citation statements)

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“…It would be also interesting to test the stability of results having in view the well-known property of noise immunity of GMDH-based algorithms. This paper continues our applied research presented in [22].…”

Section: Gmdh-based Classifiers With Applicationssupporting

confidence: 68%

“…In our paper [22], we began to study the possibilities of GMDH-based algorithms on opinion mining of typical texts related to health social networks. In the paper we conducted our experiments on the same dataset AskaPatient used in this paper.…”

Section: Discussionmentioning

confidence: 99%

“…In the current research, we tried to explore all mentioned problems by putting special attention to parameter tuning, in particular, experiments with different type of parametrization: character or word n-grams. In the paper [22] we used only one-word terms. To select these terms, we used the criterion of term specificity which considers term frequency in a given document corpus and any basic corpus [28,29].…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Opinion Mining on Small and Noisy Samples of Health-Related Texts

Akhtyamova

Alexandrov

Cardiff

et al. 2018

Advances in Intelligent Systems and Computing

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The topic of people's health has always attracted the attention of public and private structures, the patients themselves and, therefore, researchers. Social networks provide an immense amount of data for analysis of health-related issues; however it is not always the case that researchers have enough data to build sophisticated models. In the paper, we artificially create this limitation to test performance and stability of different popular algorithms on small samples of texts. There are two specificities in this research apart from the size of a sample: (a) here, instead of usual 5-star classification, we use combined classes reflecting a more practical view on medicines and treatments; (b) we consider both original and noisy data. The experiments were carried out using data extracted from the popular forum AskaPatient. For tuning parameters, GridSearchCV technique was used. The results show that in dealing with small and noisy data samples, GMDH Shell is superior to other methods. The work has a practical orientation.

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Section: Gmdh-based Classifiers With Applicationssupporting

confidence: 68%

Section: Discussionmentioning

confidence: 99%