A KERNEL FISHER DISCRIMINANT ANALYSIS-BASED TREE ENSEMBLE CLASSIFIER: KFDA FOREST
- Authors
- Kim, Donghwan; Park, Seung Hwan; Baek, Jun-Geol
- Issue Date
- 2018
- Publisher
- UNIV CINCINNATI INDUSTRIAL ENGINEERING
- Keywords
- classification; ensemble classifier; decision trees; kernel fisher discriminant analysis; rotation forest
- Citation
- INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE, v.25, no.5, pp.569 - 579
- Indexed
- SCIE
SCOPUS
- Journal Title
- INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE
- Volume
- 25
- Number
- 5
- Start Page
- 569
- End Page
- 579
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/81050
- ISSN
- 1072-4761
- Abstract
- In general, an ensemble classifier is more accurate than a single classifier. In this study, we propose an ensemble classifier called the kernel Fisher discriminant analysis forest (KFDA Forest), which is a tree-based ensemble method that applies KFDA. To promote diversity, bootstrap is used, and variable sets are randomly divided into K subsets. KFDA is performed on each subset to increase classification accuracy. KFDA maximizes the distance between classes while minimizing the distance within classes. KFDA can also be applied to classification problems in a nonlinear data structure using the kernel trick because it can transform the input space into a kernel feature space, commonly named a rotation, rather than performing a dimensionality reduction. Because new feature axes and KFDA projections are parallel, decision trees are used as a base classifier. To compare the proposed method with existing ensemble methods, we apply these to real datasets from the UCI and KEEL repositories.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.