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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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A Combined Approach for Multi-Label Text Data Classification.

Rokas Štrimaitis1, Pavel Stefanovič1, Simona Ramanauskaitė2

  • 1Department of Information Systems, Vilnius Gediminas Technical University, Saulėtekio Al. 11, LT-10223 Vilnius, Lithuania.

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Summary
This summary is machine-generated.

This study introduces a novel multilabel text classification approach for Lithuanian financial news, enhancing accuracy by combining similarity measures with traditional algorithms. The combined method significantly improves classification performance.

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Area of Science:

  • Natural Language Processing
  • Machine Learning
  • Data Mining

Background:

  • Automated data analysis relies heavily on data quality.
  • Multilabel classification, assigning multiple classes to a single data item, presents unique challenges.
  • Existing solutions for Lithuanian text data lack dedicated multilabel classification capabilities.

Purpose of the Study:

  • To propose a novel combined approach for multilabel text data classification.
  • To enhance the accuracy of traditional classification algorithms for Lithuanian text.
  • To address the specific need for multilabel classification in Lithuanian financial news analysis.

Main Methods:

  • Developed a combined approach integrating similarity measures with classification algorithms.
  • Utilized financial news data in Lithuanian, manually classified into ten classes (max two per item).
  • Compared five algorithms (SVM, Naive Bayes, k-NN, Decision Trees, LDA) and two similarity measures (cosine, Dice).

Main Results:

  • Cosine similarity and multinomial Naive Bayes yielded the best individual results.
  • The proposed combined approach integrating these two methods achieved a statistically significant increase in global accuracy.
  • Analysis revealed peculiarities in the application of the combined approach across different cases.

Conclusions:

  • The combined approach effectively improves multilabel text classification accuracy for Lithuanian financial news.
  • Integration of similarity measures with established algorithms offers a promising direction for text analysis.
  • The developed method provides a valuable solution for Lithuanian language data classification tasks.