Struktura obiektu

Autor:

Kajdanowicz, Tomasz ; Kazienko, Przemysław

Współtwórca:

Cordón, Oskar - ed. ; Kazienko, Przemysław - ed.

Tytuł:

Multi-label classification using error correcting output codes

Podtytuł:

Hybrid and Ensemble Methods in Machine Learning

Tytuł publikacji grupowej:

AMCS, Volume 22 (2012)

Temat i słowa kluczowe:

machine learning ; supervised learning ; multi-label classification ; error-correcting output codes ; ECOC ; ensemble methods ; binary relevance ; framework

Abstract:

A framework for multi-label classification extended by Error Correcting Output Codes (ECOCs) is introduced and empirically examined in the article. The solution assumes the base multi-label classifiers to be a noisy channel and applies ECOCs in order to recover the classification errors made by individual classifiers. ; The framework was examined through exhaustive studies over combinations of three distinct classification algorithms and four ECOC methods employed in the multi-label classification problem. ; The experimental results revealed that (i) the Bode?Chaudhuri?Hocquenghem (BCH) code matched with any multi-label classifier results in better classification quality; (ii) the accuracy of the binary relevance classification method strongly depends on the coding scheme; (iii) the label power-set and the RAkEL classifier consume the same time for computation irrespective of the coding utilized; (iv) in general, they are not suitable for ECOCs because they are not capable to benefit from ECOC correcting abilities; (v) the all-pairs code combined with binary relevance is not suitable for datasets with larger label sets.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2012

Typ zasobu:

artykuł

DOI:

10.2478/v10006-012-0061-2

Strony:

829-840

Źródło:

AMCS, Volume 22, Number 4 (2012) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego