Ordinal Data Classification Using Kernel Discriminant Analysis: A Comparison of Three Approaches

• Type: Article in International Conference Proceedings Book
• Date: 2012

Authors

Cardoso, JS (Author) FEUP; Sousa, R (Author) Other; Domingues, I (Author) Other

Conference proceedings International

Title: 11th International Conference on Machine Learning and Applications, ICMLA, Boca Raton, FL, USA, December 12-15, 2012. Volume 1

Pages: 473-477

11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012

Boca Raton, FL, 12 December 2012 through 15 December 2012

Indexing

ISI Web of Knowledge - 11 Citations

Scopus - 15 Citations

Other information

Authenticus ID: P-008-99S

DOI: 10.1109/icmla.2012.86

Abstract (EN): Ordinal data classification (ODC) has a wide range of applications in areas where human evaluation plays an important role, ranging from psychology and medicine to information retrieval. In ODC the output variable has a natural order, however, there is not a precise notion of the distance between classes. The recently proposed method for ordinal data, Kernel Discriminant Learning Ordinal Regression (KDLOR), is based on Linear Discriminant Analysis (LDA), a simple tool for classification. KDLOR brings LDA to the forefront in the ODC field, motivating further research. This paper compares three LDA based algorithms for ODC. The first method uses the generic framework of Frank and Hall for ODC instantiated with a kernel version of LDA. Similarly, the second method is based on the also generic Data Replication framework for ODC instantiated with the same kernel version of LDA. Both the Frank and Hall and Data Replication methods address the ODC problem by the use of a base binary classifier. Finally, the third method under comparison is KDLOR. The experiments are carried out on synthetic and real datasets. A comparison between the performances of the three systems is made based on t-statistics. The performance and running time complexity of the methods do not support any advantage of KDLOR over the other two methods. © 2012 IEEE.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 5