A Survey of Decision Fusion and Feature Fusion Strategies for Pattern Classification

Abstract

For any pattern classification task, an increase in data size, number of classes, dimension of the feature space, and interclass separability affect the performance of any classifier. A single classifier is generally unable to handle the wide variability and scalability of the data in any problem domain. Most modern techniques of pattern classification use a combination of classifiers and fuse the decisions provided by the same, often using only a selected set of appropriate features for the task. The problem of selection of a useful set of features and discarding the ones which do not provide class separability are addressed in feature selection and fusion tasks. This paper presents a review of the different techniques and algorithms used in decision fusion and feature fusion strategies, for the task of pattern classification. A survey of the prominent techniques used for decision fusion, feature selection, and fusion techniques has been discussed separately. The different techniques used for fusion have been categorized based on the applicability and methodology adopted for classification. A novel framework has been proposed by us, combining both the concepts of decision fusion and feature fusion to increase the performance of classification. Experiments have been done on three benchmark datasets to prove the robustness of combining feature fusion and decision fusion techniques.

Keywords:

• Accuracy
• Classifier selection
• Combining classifiers
• Decision fusion
• Feature fusion
• Feature ranking
• Feature selection

Additional information

Notes on contributors

Utthara Gosa Mangai

Utthara Gosa Mangaireceived her B. Tech degree in Information and Technology from Adhiyamaan College of Engineering, Anna University, Hosur, Tamil Nadu, India in 2005. Currently, she is pursuing her Master of Science (MS) degree in Computer Science and Engineering from Indian Institute of Technology, Madras, Chennai, India. Her research interests are in Classifier Combination, Pattern Recognition and Image Segmentation.

E-mail: [email protected]

Suranjana Samanta

Suranjana Samanta received her B. Tech degree in Computer Science and Engineering from Kalyani Government Engineering College, West Bengal, in 2007. She completed her Master of Science (MS) degree in Computer Science and Engineering from Indian Institute of Technology, Madras, Chennai, India in 2010. Her research interests are in Computer Vision, Machine Learning and Pattern Recognition.

E-mail: [email protected]

Sukhendu Das

Sukhendu Das was born in 1962, in Kharagpur, W.B., India. He is currently working as an Associate Professor in the Deptt. Of Computer Science and Engg., IIT Madras, Chennai, India. He completed his B.Tech degree from IIT Kharagpur in the Deptt. Of Electrical Engg. in 1985 and M. Tech Degree in the area of Computer Technology from IIT Delhi in 1987. He then obtained his Ph.D degree from IIT Kharagpur in 1993. His current areas of research interests are: Visual Perception, Computer Vision: Digital Image and Video Processing and Pattern Recognition, Computer Graphics,Artificial Neural Networks, Computational Science and engineering and Soft Computing. Dr. Sukhendu Das has been a faculty of the Deptt. of CS&E, IIT Madras, India since 1989. He has worked as a visiting scientist in the University of Applied Sciences, Pforzheim, Germany, for post-doctoral research work, from Dec. 2001 till May 2003; and in the Univ. of UWA, Perth Australia, during June-Aug. 2006 and July-Sept. 2008. He has completed guiding two Ph.D (currently guiding two more) students, 21 M.S. (currently guiding five), several M. Tech and B. Tech students. He had completed several international and national sponsored projects and consultancies, both as principle and co-investigators. Currently, he is involved in three (3) sponsored projects/consultancies in IIT Madras. He has published about 100 technical papers in international and national journals and conferences. He has reviewed several papers in international journals (IEEE, Elsevier etc.) and chaired several sessions in conferences. He has received two best papers and a best design contest award.

E-mail: [email protected]

Pinaki Roy Chowdhury

Pinaki Roy Chowdhury was born in Calcutta in July 1968. He did his B.Sc. (Hons.) in Physics from Delhi University in 1988. Subsequently he completed his M.Sc. in Computer Science from School of Computer Science, Devi Ahilya Vishwavidyalaya, Indore in 1990 as a sponsored candidate of DRDO. He joined DRDO in TBRL, Chandigarh in August 1990 and subsequently moved to DTRL, Delhi in April 1992. He completed his Ph.D. in Computer Engineering from IT-BHU in 2001. He has worked in the field of machine learning, pattern recognition, global optimization, softcomputing and their applications in the area of image processing. He is a Life Member of Indian Society of Remote Sensing (ISRS), a Senior Member of IEEE and a Professional Member of ACM.

E-mail: [email protected]