Statistical Classification of Scientific Publications

Balys, Vaidas; Rudzkis, Rimantas

doi:10.15388/informatica.2010.300

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…The outcome of these approaches is excellent when utilizing the entire content of the research articles. In the case of research article classification, Balys and Rudzkis [31] presented a study on the automatic classification of research articles using applied mathematical analysis of probabilistic distributions of the scientific terms in texts. Moreover, some works, such as [32], focused on ML algorithms to develop subject classification rules for research article classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Document Classification: Unleashing the Power of Genetic Algorithms

et al. 2023

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Section: Literature Reviewmentioning

confidence: 99%

Optimizing Document Classification: Unleashing the Power of Genetic Algorithms

et al. 2023

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“…These approaches reported good results but employed the whole text of the documents. In the context of classification of research papers, Balys and Rudzkis in [24] proposed an automatic classification of scientific text-primarily based on applied math analysis of probabilistic distributions of scientific terms in texts. Some works like [25] focus on machine learning algorithms to develop subject classification rules for documents.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Evaluation of Metadata-Based Features to Classify Research Paper’s Topics

et al. 2021

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The existing plethora of document classification techniques exploits different data sources either from the content or metadata of research articles. Various journal publishers like Springer, Elsevier, IEEE, etc., do not provide open access to the content of research articles, whereas metadata is freely available there. Metadata like title, keyword, and abstract can serve as a better alternative to the content in various scenarios. In the current literature, researchers have assessed the role of some of the metadata individually. We believe that the collective contribution of metadata parameters can play a significant role in classifying research papers. This paper presents a comprehensive evaluation of the role of metadata, individually as well as in combinations to achieve the objective of research paper classification. Moreover, we have classified the research articles into ACM hierarchy root categories (e.g. general literature, hardware, software, etc.). In this comprehensive evaluation, we have assessed all the possible combinations of metadata features against different classifiers such as Random Forest, K Nearest Neighbor, and Decision Tree. The results of this research reveal that the title keywords combination outperforms other combinations with an F-measure score of 0.88.

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“…On the other hand, the collections of scientific publications are often used as experimental collection to illustrate text mining techniques, measures and algorithms like (Sebastiani, 2005) and (Hazewinkel, 2002). Also a large amount of specific text mining algorithms, pre-treatment processes and techniques were involved like in (Balys et al, 2010), (Galindo et al, 2010), (Taheriyan, 2011). Balys and Rudzkis in (Balys et al, 2010) have suggested a classifier adapted to scientific papers, their classifier is based on some probabilistic models in the aim to choose the positions of the terms to take into account (the same for all document), these positions are called projection on a set of indices; the classification was done inside the classes of applied mathematics and statistics.…”

Section: Related Papersmentioning

confidence: 99%

“…Also a large amount of specific text mining algorithms, pre-treatment processes and techniques were involved like in (Balys et al, 2010), (Galindo et al, 2010), (Taheriyan, 2011). Balys and Rudzkis in (Balys et al, 2010) have suggested a classifier adapted to scientific papers, their classifier is based on some probabilistic models in the aim to choose the positions of the terms to take into account (the same for all document), these positions are called projection on a set of indices; the classification was done inside the classes of applied mathematics and statistics. In (Galindo et al, 2010) only the punctuation signs are used to classify inside huge classes of domains like medicine, engineering, and social science.…”

Section: Related Papersmentioning

confidence: 99%

Analysis of Mexican Research Production - Exploring a Scientifical Database

Brambila

Juganaru-Mathieu²,

González-Brambila

2013

Proceedings of the International Conference on Knowledge Discovery and Information Retrieval and the International Conference O

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International audienceThis paper presents an exploring analysis of the research activity of a country using ISI web of Science Collection. We decided to focus the work on Mexican research in computer science. The aim of this text mining work is to extract the main direction in this scientific field. The focal exploring axe is: clustering. We have done two folds analysis: the first one on frequency representation of the extracted terms, and the second, much larger and difficult, on mining the document representations with the aim of finding clusters of documents, using the most used terms in the title. The cluster algorithms applied were hierarchical, kmeans, DIANA, SOM, SOTA, PAM, AGNES and model. Experiments with different number of terms and with the complete dataset were realized, but results were not satisfactory. We conclude that the best model for this type of analysis is model based, because it gives a better classification, but still it needs better performance algorithms. Results show that very few areas are developed by Mexicans

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Statistical Classification of Scientific Publications

Cited by 8 publications

References 11 publications

Optimizing Document Classification: Unleashing the Power of Genetic Algorithms

Optimizing Document Classification: Unleashing the Power of Genetic Algorithms

A Comprehensive Evaluation of Metadata-Based Features to Classify Research Paper’s Topics

Analysis of Mexican Research Production - Exploring a Scientifical Database

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