One versus one multi-class classification fusion using optimizing decision directed acyclic graph for predicting listing status of companies

Information Fusion

Volumn 36, Issue , 2017, Pages 80-89

  Zhou, Ligang ^a   Wang, Qingyang ^b   Fujita, Hamido ^c  

^a MACAU UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Macao)

^b UNIVERSITY OF MACAU   (Macao)

^c IWATE PREFECTURAL UNIVERSITY   (Japan)

Author keywords

Decision directed acyclic graph; Listing status; Multi class classification; One versus one; Optimizing; Prediction

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLASSIFIERS; DECISION MAKING; DECISION TREES; FORECASTING; GRAPH THEORY; LEARNING SYSTEMS;

DECISION DIRECTED ACYCLIC GRAPHS; LISTING STATUS; MULTI-CLASS CLASSIFICATION; ONE VERSUS ONE; OPTIMIZING;

DIRECTED GRAPHS;

EID: 84995752440     PISSN: 15662535     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.inffus.2016.11.009     Document Type: Article

References (39)

1. [1] Aly, M., Survey on multiclass classification methods. Technical Report, 2005, Caltech.
2. [2] Matsui, H., Araki, T., Konishi, S., Multiclass functional discriminant analysis and its application to gesture recognition. J. Classification 28:2 (2011), 227–243.
3. [3] Ghorai, S., Mukherjee, A., Dutta, P.K., Discriminant analysis for fast multiclass data classification through regularized kernel function approximation. IEEE Trans. Neural Netw. 21:6 (2010), 1020–1029.
4. [4] Guo, Y.Q., Hastie, T., Tibshirani, R., Regularized linear discriminant analysis and its application in microarrays. Biostatistics 8:1 (2007), 86–100.
5. [5] Suresha, M., Danti, A., Narasimhamurthy, S.K., Decision trees to multiclass prediction for analysis of arecanut data. Comput. Syst. Sci. Eng. 29:1 (2014), 105–114.
6. [6] Breiman, L., Friedman, J., Olshen, R., Stone, C., Classification and Regression Trees. 1984, CRC Press, Boca Raton, Florida.
7. [7] Maimon, O., Rokach, L., Data Mining and Knowledge Discovery Handbook. 2005, Springer, New York.
8. [8] Ahn, W.H., Nah, S.P., Seo, B.S., Automatic classification of digitally modulated signals based on k-nearest neighbor. Lect. Notes Electr. Eng. 329 (2015), 63–69.
9. [9] V. Metsis, I. Androutsopoulos, G. Paliouras, Spam filtering with naive bayes - which naive bayes?, in: The Third Conference on Email and Anti-Spam - Proceedings, CEAS 2006, Mountain View, California.
10. [10] Almeida, T.A., Almeida, J., Yamakami, A., Spam filtering: how the dimensionality reduction affects the accuracy of naive bayes classifiers. J. Internet Serv. Appl. 1:3 (2011), 183–200.
11. [11] Lin, M.L., Tang, K., Yao, X., Dynamic sampling approach to training neural networks for multiclass imbalance classification. IEEE Trans. Neural Netw. Learn. Syst. 24:4 (2013), 647–660.
12. [12] Kim, W., Lee, H.K., Park, J., Yoon, K., Multiclass adult image classification using neural networks. Lect. Notes Comput. Sci. 3501 (2005), 222–226.
13. [13] Ou, G.B., Murphey, Y.L., Multi-class pattern classification using neural networks. Pattern Recognit. 40:1 (2007), 4–18.
14. [14] Hsu, C.W., Lin, C.J., A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13:2 (2002), 415–425.
15. [15] Anand, A., Suganthan, P.N., Multiclass cancer classification by support vector machines with class-wise optimized genes and probability estimates. J. Theor. Biol. 259:3 (2009), 533–540.
16. [16] Guler, I., Ubeyli, E.D., Multiclass support vector machines for EEG-signals classification. IEEE Trans. Inf. Technol. Biomed. 11:2 (2007), 117–126.
17. [17] Du, S.C., Huang, D.L., Lv, J., Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines. Comput. Ind. Eng. 66:4 (2013), 683–695.
18. [18] Garcia, L.P.F., Sáez, J.A., Luengo, J., Lorena, A.C., A.C.P.L.F. de Carvalho, Herrera, F., Using the one-vs-one decomposition to improve the performance of class noise filters via an aggregation strategy in multi-class classification problems. Knowl. Based Syst. 90 (2015), 153–164.
19. [19] Paz Sesmero, M., Alonso-Weber, J.M., Gutierrez, G., Ledezma, A., Sanchis, A., An ensemble approach of dual base learners for multi-class classification problems. Inf. Fusion 24 (2015), 122–136.
20. [20] Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F., An overview of ensemble methods for binary classifiers in multi-class problems: experimental study on one-vs-one and one-vs-all schemes. Pattern Recognit. 44:8 (2011), 1761–1776.
21. [21] Galar, M., Derrac, J., Peralta, D., Triguero, I., Paternain, D., Lopez-Molina, C., Garca, S., Bentez, J.M., Pagola, M., Barrenechea, E., Bustince, H., Herrera, F., A survey of fingerprint classification part I: taxonomies on feature extraction methods and learning models. Knowl. Based Syst. 81 (2015), 76–97.
22. [22] Galar, M., Derrac, J., Peralta, D., Triguero, I., Paternain, D., Lopez-Molina, C., Garca, S., Bentez, J.M., Pagola, M., Barrenechea, E., Bustince, H., Herrera, F., A survey of fingerprint classification part II: experimental analysis and ensemble proposal. Knowl. Based Syst. 81 (2015), 98–116.
23. [23] Sáez, J.A., Galar, M., Luengo, J., Herrera, F., Analyzing the presence of noise in multi-class problems: alleviating its influence with the one-vs-one decomposition. Knowl. Inf Syst. 38:1 (2014), 179–206.
24. [24] Sun, P., Reid, M.D., Zhou, J., An improved multiclass logitboost using adaptive-one-vs-one. Mach. Learn. 97:3 (2014), 295–326.
25. [25] Shiraishi, Y., Game theoretical analysis of the simple one-vs.-all classifier. Neurocomputing 71:13–15 (2008), 2747–2753.
26. [26] Allwein, E.L., Schapire, R.E., Singer, Y., Reducing multiclass to binary: a unifying approach for margin classifiers. J. Mach. Learn. Res. 1 (2000), 113–141.
27. [27] Lorena, A.C., A.C.P.L.F. De Carvalho, Gama, J.M., A review on the combination of binary classifiers in multiclass problems. Artif. Intell. Rev. 30 (2008), 19–37.
28. [28] Galar, M., Fernández, A., Barrenechea, E., Bustince, H., Herrera, F., Dynamic classifier selection for one-vs-one strategy: avoiding non-competent classifiers. Pattern Recognit. 46:12 (2013), 3412–3424.
29. [29] Platt, J.C., Cristianini, N., Shawe-Taylor, J., Large margin DAGs for multiclass classification. Advances in Neural Information Processing Systems, 2000, 547–553.
30. [30] Takahashi, F., Abe, S., Optimizing directed acyclic graph support vector machines. Artificial Neural Networks in Pattern Recognition (ANNPR), 2003, 166–173.
31. [31] Zhang, L., Altman, E.I., Yen, J., Corporate financial distress diagnosis model and application in credit rating for listing firms in china. Front. Comput. Sci. China 4:2 (2010), 220–236.
32. [32] Chen, J., Marshall, B.R., Zhang, J., Ganesh, S., Financial distress prediction in china. Rev. Pacific Basin Financ. Markets Policies 9:2 (2006), 317–336.
33. [33] Zhou, L., Lai, K.K., Yen, J., Empirical models based on features ranking techniques for corporate financial distress prediction. Comput. Math. Appl. 64:8 (2012), 2484–2496.
34. [34] Zhou, L., Tam, K.P., Fujita, H., Predicting the listing status of chinese listed companies with multi-class classification models. Inf. Sci. 328 (2016), 222–236.
35. [35] Hüllermeier, E., Vanderlooy, S., Combining predictions in pairwise classification: an optimal adaptive voting strategy and its relation to weighted voting. Pattern Recognit. 43:1 (2010), 128–142.
36. [36] Statistics and Machine Learning Toolbox Release 2014a, 2014, The MathWorks, Inc., Natick, Massachusetts, United States.
37. [37] A. Asuncion, D. Newman, UCI machine learning repository, 2007.
38. [38] Shumway, T., Forecasting bankruptcy more accurately: a simple hazard model. J. Bus. 74:1 (2001), 101–124.
39. [39] Li, H., Lee, Y.C., Zhou, Y.C., Sun, J., The random subspace binary logit (RSBL) model for bankruptcy prediction. Knowl. Based Syst. 24:8 (2011), 1380–1388.