Refine your search
Collections
Co-Authors
Journals
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Sridevi, T.
- Rough Set Theory Based Attribute Reduction for Breast Cancer Diagnosis
Abstract Views :359 |
PDF Views:75
Authors
T. Sridevi
1,
A. Murugan
2
Affiliations
1 Mother Teresa Women’s University, Kodaikanal, Tamil Nadu, IN
2 Department of Computer Science, Dr. Ambedkar Govt. Arts College, Chennai, Tamil Nadu, IN
1 Mother Teresa Women’s University, Kodaikanal, Tamil Nadu, IN
2 Department of Computer Science, Dr. Ambedkar Govt. Arts College, Chennai, Tamil Nadu, IN
Source
Indian Journal of Innovations and Developments, Vol 1, No 5 (2012), Pagination: 309-313Abstract
Data mining (DM) techniques are used to determine interesting patterns from different domains according to the need of applications and the analyst. Medical field is one among the major user of the mining technology for diagnosing the attributes for the medical issues. Breast cancer is one of the most important medical problems. The modern researchers and technological advancements attempted to determine the cause and prevention in an effective manner with lesser number of attributes. But the diagnosis is lengthy process with multiple and multilevel attribute analysis in certain cases. In order to improve the accuracy of diagnosis with limited attributes, in this paper rough set based relative reduct algorithm is used to reduce the number of attributes using equivalence relation. The effectiveness of proposed Rough Set Reduction algorithm is analyzed on Wisconsin Breast Cancer Dataset (WBCD) and presented as a part of the paper. The experimental results show that the relative reduct performs better attribute reduction.Keywords
Data mining, Data Preprocessing, Rough Set, Data reduction, Breast Cancer DiagnosisReferences
- Agrawal R, Imielinski T and Swami A (1993) Database mining: A performance perspective. IEEE Trans. Knowl. Data Eng. 5(6), 914–925.
- Blake CL and Merz CJ (1998) UCI Repository of machine learning databases, Irvine, University of California, http://www.ics.uci.edu/~mlearn/
- Dash M and Liu H (1997) Feature Selection for Classification. Intell. Data anal. 1(3), 131-156.
- Fayyad UM, Piatetsky-Shapiro G, Smyth P and Uthurusamy R (1996) Advances in Knowledge Discovery and Data Mining, pages 495–515. AAAI Press / the MIT Press.
- Guyon I and Elissee A (2003) An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157-1182.
- Han J, Hu X and Lin TY (2004) Feature subset selection based on relative dependency between attributes, in Proc. of the 4th International Conf. on Rough sets and Current Trend in Computing, Uppsala, pp. 176–185.
- Jensen R and Shen Q (2001) A Rough Set-Aided System for Sorting WWW Bookmarks, In Zhong N et al. (Eds.), Web Intelligence: Research and Development, pp. 95- 105.
- Jensen R (2004) Combining rough and fuzzy sets for feature selection, Ph.D thesis, University of Edinburgh.
- Liu H and Motoda H (1998) Feature Selection for Knowledge Discovery and Data Mining. Boston: Kluwer Academic Publishers.
- Mandelbrot BB (1965) Linear and nonlinear separation of patterns by linear programming. Oper. Res. 13, 444- 452.
- Qiang Shen and Alexios Chouchoulas (2000) A modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems. Eng. Appl. Artif. Intell. 13(3), 263–278.
- Quinlan, JR (1993) C4.5: Programs for Machine Learning, The Morgan Kaufmann Series in Machine earning. Morgan Kaufmann Publishers, San Mateo, CA.
- Street W, Wolberg W and Mangasarian O (1993) Nuclear feature extraction for breast tumor diagnosis. Available from: citeseer.ist.psu.edu/street93nuclear.html.
- Zdzislaw Pawlak (1982) Rough sets. Int. J. Compu.Info. Sci. 11, 341-356.
- Zdzislaw Pawlak (1991) Rough Sets-Theoretical Aspects and Reasoning about Data, Kluwer Academic Publications.
- Number Plate Recognition for Vehicular Surveillance System Using an Improved Segmentation
Abstract Views :146 |
PDF Views:2
Authors
Affiliations
1 Department of ECE, KL University, Vijayawada, A.P, IN
2 Department of ECE, KL University, Vijayawada, A.P, IN
1 Department of ECE, KL University, Vijayawada, A.P, IN
2 Department of ECE, KL University, Vijayawada, A.P, IN
Source
Digital Image Processing, Vol 4, No 7 (2012), Pagination: 367-371Abstract
Number Plate Recognition systems are used to track and monitor the moving vehicles by automatically extracting the number plates. The objective of this system is to recognize vehicles based on license plate information. Number plate recognition is part of vehicle identification system. Now a days it has wide range of applications like traffic surveillance, access control etc. The images of passing vehicles are taken at surveillance system and those images will be processed. The Proposed method uses simple morphological open and close operations using different structuring elements for plate feature extraction, Labeling the connected pixels, searching the plate location based on Geometrical conditions, segmenting the number plate and character recognition with Neural Network of Multilayer Perceptron. We have proposed a new method for plate segmentation based on Labeling. This method has been tested using a database of Indian number plates and results achieved have shown the high detection rate than existing methods.Keywords
Morphological Operations, Labeling, Plate Segmentation, Multi Layer Perceptron.- Segmentation and Object Recognition Using Edge Detection Techniques
Abstract Views :414 |
PDF Views:317
Authors
Affiliations
1 Department of CSE, Chaitanya Bharathi Institute of Technology, Gandipet, Hyderabad, IN
1 Department of CSE, Chaitanya Bharathi Institute of Technology, Gandipet, Hyderabad, IN