Sage Journals: Discover world-class research

Abstract

Binary Relevance (BR) is a simple and direct approach to the Multi-Label Classification (MLC). It decomposes the multi-label problem into several binary problems, one per label. It uses an algorithm of traditional supervised classification in order to solve these binary problems. On the other hand, Credal C4.5 (CC4.5) is a modification of the classical C4.5. CC4.5 estimates the probability of the class variable by using imprecise probabilities. In the literature, this new classification algorithm has obtained better results than C4.5 when both have been applied on datasets with class noise. In MLC, since there are not just a class, but multiple labels are disposed, it is more probable that there is intrinsic noise than in traditional classification. From the previous reasons, in this work it is studied the performance of BR using Credal C4.5 as base classifier versus BR with C4.5. It is carried out an experimental study with several muti-label datasets and a considerable number of measures for MLC. This study shows that the performance of BR is improved when it uses CC4.5 as base classifier versus BR with C4.5. In consequence, it is probably suitable to apply imprecise probabilities in Decision Trees within the MLC field too.

Keywords

Multi-label classification Binary Relevance Credal C4.5 C4.5 imprecise probabilities

Get full access to this article

View all access options for this article.

References

Abellán , Uncertainty measures on probability intervals from the imprecise dirichlet model, International Journal of General Systems 35(5) (2006), 509–528.

Abellán , Ensembles of decision trees based on imprecise probabilities and uncertainty measures, Information Fusion 14(4) (2013), 423–430.

Abellán and

C.J.

Mantas , Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring, Expert Systems with Applications 41(8) (2014), 3825–3830.

Abellán and

Masegosa , An experimental study about simple decision trees for bagging ensemble on datasets with classification noise, In Symbolic and Quantitative Approaches to Reasoning with Uncertainty, volume 5590 of Lecture Notes in Computer Science, Springer, 2009, pp. 446–456.

Abellán and

Moral , Building classification trees using the total uncertainty criterion, International Journal of Intelligent Systems 18(12) (2003), 1215–1225.

R.T.

Alves ,

M.R.

Delgado and

A.A.

Freitas , Knowledge discovery with arti_cial immune systems for hierarchical multi-label classi_cation of protein functions, In International Conference on Fuzzy Systems, 2010, pp. 1–8.

Barutcuoglu ,

R.E.

Schapire and

O.G.

Troyanskaya , Hierarchical multi-label prediction of gene function, Bioinformatics 22(7) (2006), 830–836.

M.R.

Boutell ,

Luo ,

Shen and

C.M.

Brown , Learning multi-label scene classification, Pattern Recognition 37(9) (2004), 1757–1771.

Charte ,

A.J.

Rivera ,

Charte ,

M.J.

del Jesus and

Herrera , Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets r package and the cometa data repository. Neurocomputing, In Press, 2018.

10.

Demšar , Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research 7 (2006), 1–30.

11.

I.N.

Inza ,

Sierra ,

Blanco and

Larrañaga , Gene selection by sequential search wrapper approaches in microarray cancer class prediction, Journal of Intelligent and Fuzzy Systems 12(1) (2002), 25–33.

12.

G.J.

Klir , Uncertainty and Information: Foundations of Generalized Information Theory. John Wiley And Sons, Inc., 2005.

13.

Madjarov ,

Kocev ,

Gjorgjevikj and

Džeroski , An extensive experimental comparison of methods for multi-label learning, Pattern Recognition 45(9) (2012), 3084–3104.

14.

C.J.

Mantas and

Abellán , Credal-C4.5: Decision tree based on imprecise probabilities to classify noisy data, Expert Systems with Applications 41(10) (2014), 4625–4637.

15.

C.J.

Mantas ,

Abellán and

J.G.

Castellano , Analysis of Credal-C4.5 for classification in noisy domains, Expert Systems with Applications 61 (2016), 314–326.

16.

McCallum , Multi-label text classification with a mixture model trained by EM, In AAAI'99 Workshop on Text Learning, 1999, pp. 1–7.

17.

Nasierding and

Kouzani , Image to Text Translation by Multi-Label Classification, In Advanced Intelligent Computing Theories and Applications With Aspects of Artificial Intelligence, volume 6216 of Lecture Notes in Computer Science, chapter 31, Springer, 2010, pp. 247–254.

18.

Pachowicz and

Bala , A noise-tolerant approach to symbolic learning from sensory data, Journal ofIntelligent and Fuzzy Systems 2 (1994), 347–361.

19.

J.R.

Quinlan , C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993.

20.

Read ,

Pfahringer ,

Holmes and

Frank. , Classifier chains for multi-label classification, Machine Learning 85(3) (2011), 333.

21.

Ruiz ,

Riquelme and

Aguilar-Ruiz , Projectionbased measure for efficient feature selection, Journal ofIntelligent and Fuzzy Systems 12 (2002), 175–183.

22.

R.E.

Schapire and

Singer , Boostexter: A boostingbased system for text categorization, Machine Learning 39(2) (2000), 135–168.

23.

C.E.

Shannon , A mathematical theory of communication, Bell System Technical Journal 27(3) (1948), 379–423.

24.

Tsoumakas ,

Spyromitros-Xioufis ,

Vilcek and

Vlahavas , Mulan: A java library for multi-label learning, Journal of Machine Learning Research 12 (2011), 2411–2414.

25.

Walley , Inferences from multinomial data; learning about a bag of marbles (with discussion), Journal of the Royal Statistical Society Series B (Methodological) 58(1) (1996), 3–57.

26.

Wilcoxon , Individual comparisons by ranking methods, Biometrics Bulletin 1(6) (1945), 80–83.

27.

I.H.

Witten and

Frank , Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann Series in Data Management Systems Morgan Kaufmann Publishers Inc, San Francisco, CA, USA, second edition, 2005.

28.

M.L.

Zhang and

Z.H.

Zhou , A review on multi-label learning algorithms, IEEE Transactions on Knowledge and Data Engineering 26(8) (2014), 1819–1837.