High-dimensional Canonical Forest

ABSTRACT

Recently, a new ensemble classification method named Canonical Forest (CF) has been proposed by Chen et al. [Canonical forest. Comput Stat. 2014;29:849–867]. CF has been proven to give consistently good results in many data sets and comparable to other widely used classification ensemble methods. However, CF requires an adopting feature reduction method before classifying high-dimensional data. Here, we extend CF to a high-dimensional classifier by incorporating a random feature subspace algorithm [Ho TK. The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Mach Intell. 1998;20:832–844]. This extended algorithm is called HDCF (high-dimensional CF) as it is specifically designed for high-dimensional data. We conducted an experiment using three data sets – gene imprinting, oestrogen, and leukaemia – to compare the performance of HDCF with several popular and successful classification methods on high-dimensional data sets, including Random Forest [Breiman L. Random forest. Mach Learn. 2001;45:5–32], CERP [Ahn H, et al. Classification by ensembles from random partitions of high-dimensional data. Comput Stat Data Anal. 2007;51:6166–6179], and support vector machines [Vapnik V. The nature of statistical learning theory. New York: Springer; 1995]. Besides the classification accuracy, we also investigated the balance between sensitivity and specificity for all these four classification methods.

KEYWORDS:

• Canonical Forest
• canonical linear discriminant analysis
• classification
• ensemble
• high-dimensional data
• Random Subspace

AMS SUBJECT CLASSIFICATION:

• 62G99

Acknowledgements

The views presented in this paper are those of the authors and do not necessarily represent those of the U.S. Food and Drug Administration.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Yu-Chuan Chen's work was supported by Oak Ridge Institute for Science and Education. Hongshik Ahn's research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea, under the “ICT Consilience Creative Program” (IITP-2015-R0346-15-1007) supervised by IITP (Institute for Information & Communications Technology Promotion).