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Using Statistical Pattern Recognition on Gene Expression Data for Prediction of Cancer
, M.Sc. Thesis Sharif University of Technology ; Rabiee, Hamid Reza (Supervisor)
Abstract
The classification of different tumor types is of great importance in cancer diagnosis and drug discovery. However, most previous cancer classification studies are clinical based and have limited diagnostic ability. Cancer classification using gene expression data is known to contain the keys for addressing the fundamental problems relating to cancer diagnosis. The recent advent of DNA microarray technique has made simultaneous monitoring of thousands of gene expressions possible. With this abundance of gene expression data, researchers have started to explore the possibilities of cancer classification using gene expression data and quite a number of Pattern Recognition approaches have been...
A novel OCR system for calculating handwritten persian arithmetic expressions
, Article IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2009, 14 December 2009 through 16 December 2009 ; 2009 , Pages 277-282 ; 9781424459506 (ISBN) ; Tirdad, P ; Rabiee, H. R ; Parviz, M ; Sharif University of Technology
Abstract
In this paper we propose a novel OCR system which can recognize and calculate handwritten Persian arithmetic expressions without using a keyboard or a memory to store the intermediate results. Our research is composed of two major phases: character recognition and calculation. The recognition phase is based on a new approach for feature extraction. Fuzzy Support Vector Machines (FSVMs) are employed as the classifier. In calculation phase a simple algorithm is used for calculating the recognized arithmetic expression. The performance of the system is evaluated on a database consisting of 3400 digits and symbols written by 20 different people. 92 percent accuracy in recognition proves the good...
A novel OCR system for calculating handwritten Persian arithmetic expressions
, Article 8th International Conference on Machine Learning and Applications, ICMLA 2009, 13 December 2009 through 15 December 2009 ; 2009 , Pages 755-758 ; 9780769539263 (ISBN) ; Tirdad, P ; Rabiee, H. R ; Parviz, M ; Sharif University of Technology
Abstract
In this paper, we propose a novel OCR system which can recognize and calculate handwritten Persian arithmetic expressions without using a keyboard or a memory to store the intermediate results. Our system is composed of two major phases: character recognition and calculation. The recognition phase is based on a new approach for feature extraction followed by a Fuzzy Support Vector Machines (FSVMs) as the classifier. In calculation phase a simple algorithm is used for calculating the recognized arithmetic expressions. The performance of the system was evaluated on a database consisting of 3400 digits and symbols written by 20 different people. 92 percent accuracy in recognition proves the...
Fuzzy support vector machine: An efficient rule-based classification technique for microarrays
, Article BMC Bioinformatics ; Volume 14, Issue SUPPL13 , 2013 ; 14712105 (ISSN) ; Rabiee, H. R ; Anooshahpour, M ; Sharif University of Technology
2013
Abstract
Background: The abundance of gene expression microarray data has led to the development of machine learning algorithms applicable for tackling disease diagnosis, disease prognosis, and treatment selection problems. However, these algorithms often produce classifiers with weaknesses in terms of accuracy, robustness, and interpretability. This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray classification.Results: Experimental results on public leukemia, prostate, and colon cancer datasets show that fuzzy support vector machine applied in combination with filter or wrapper feature selection...