An Optimal Multi-Level Backward Feature Subset Selection for Object Recognition

ABSTRACT

Feature subset selection is an active research field in which dimensionality reduction technique is used to select a subset of relevant features. It is a data pre-processing technique used to reduce the number of features in high-dimensional data-sets, crucial in identifying the behaviour and performance of the system. Feature selection finds applications in the areas of image processing, forecasting, document classification, object recognition, anomaly detection, and bioinformatics. The benefits of using feature subset selection include improvements in the data mining algorithm's accuracy, efficiency, and scalability. Feature selection methods are classified into filter and wrapper method, based on the classifier's evaluation strategy. The existing feature subset selection methods are protracted and parameter-dependent, since the user input or threshold value is used to identify the total number of features in the final set. Current feature selection methods result in over estimating feature significance, culminating in the selection of redundant and irrelevant features. To address this issue both theoretically and experimentally, this paper introduced a novel approach to select optimal features for object recognition based on multi-level backward feature subset selection (MLBFSS) algorithm. The proposed method performs better against state-of-the-art methods, verified using benchmark real-world databases. It also outperforms other feature selection methods in terms of classification accuracy and error measures.

KEYWORDS:

• Class discrimination
• Feature extraction
• Feature subset selection
• GLCM texture features
• Harlick features
• Maximum relevancy
• Minimum redundancy

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

A. Kalaivani

A Kalaivani received her MCA degree from the University of Madras 1998, ME degree from St. Peter's University 2008, and received PhD degree from Anna University, Chennai. She has 16 years of teaching experience and currently working in the Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha University, Chennai. She has published more than 35 papers in international journals, international conferences, and book chapters. At present, research focus towards data mining and image processing.

S. Chitrakala

S Chitrakala received her BE and ME degrees from the University of Madras 1995 and 2002, respectively and recieved her PhD degree from Anna University, Chennai in 2010. She has 22 years of teaching experience and currently she is working as an associate professor in the Department of Computer Science and Engineering, Anna University Chennai. She has published more than 106 research papers in international journals and conferences and also published research papers in book chapters. At present, she teaches and leads research towards data mining, computer vision, web information retrieval, and natural language processing. She is a life member of ISTE.

E-mail: [email protected]