Effective Multi-Label Classification Method with Applications to Text Document Categorization

• Autorzy: Kinga Glinka , Danuta Zakrzewska
• Języki publikacji: EN

Abstrakty

EN

Increasing number of repositories of online documents resulted in growing demand for automatic categorization algorithms. However, in many cases the texts should be assigned to more than one class. In the paper, new multi-label classification algorithm for short documents is considered. The presented problem transformation Labels Chain (LC) algorithm is based on relationship between labels, and consecutively uses result labels as new attributes in the following classification process. The method is validated by experiments conducted on several real text datasets of restaurant reviews, with different number of instances, taking into account such classifiers as kNN, Naive Bayes, SVM and C4.5. The obtained results showed the good performance of the LC method, comparing to the problem transformation methods like Binary Relevance and Label Powerset. (original abstract)

Słowa kluczowe

EN

Algorithms; Data classifications; Text analysis

PL

Algorytmy; Klasyfikacja danych; Analiza tekstu

• Czasopismo: Information Systems in Management
• Rocznik: 2016
• Tom: 5
• Numer: nr 1
• Strony: 24--35

Twórcy

autor

Kinga Glinka

• Lodz University of Technology

autor

Danuta Zakrzewska

• Lodz University of Technology

Bibliografia

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