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Multi-label classification using error correcting output codes

Kajdanowicz T., Kazienko P.

International Journal of Applied Mathematics and Computer Science

2012

Vol. 22, no. 4

829-840

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.

Metoda agregacji sieci zależności w zastosowaniu do projektów realizacji budowy

Stefańska E.

Przegląd Budowlany

2004

R. 75, nr 11

31-35

Kombinowana metoda prognozowania macierzy nakładów z udziałem ekspertów

Ambroch K.

Badania Operacyjne i Decyzje

2000

Nr 1

5-12

Zaproponowano metodę agregacji prognoz elementów macierzy nakładów statycznego modelu przepływów międzygałęziowych. W przypadku prognoz zgodnych określono zarówno punktową, jak i przedziałową prognozę kombinowaną.

The application of the so called combined method to prediction of the expenditure matrix Elements of the static Leontief model has been suggested. The combined method with experts participation, as one of the forecast aggregation methods, has been reduced to a complex application of the results of mathematical and heuristic forecasting. In order to determine the quantities where are indispensable for comparing the results of mathematical and heuristic forecasting, various forms of the distribution of the variable being forecast, between the extreme estimations of this variable given by the experts, have been a priori assumed. As a result of an analysis of the estimations obtained from the experts (in the case of consistent forecast ) both a point combined forecast and an interval combined forecast have been determined.