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Applying Data Mining Techniques in a Real Problem
, M.Sc. Thesis Sharif University of Technology ; Salmasi, Naser (Supervisor)
Abstract
Data mining (DM) is one of the newest techniques which is used in decision making. DM helps the specialist to find the valuable knowledge which is hidden in data, with different techniques. DM has many research areas such as scientific research, medical fields, health care, fraud detection, marketing and customer relationship, sport, and games, and where ever there is data. Unfortunately, in our country, DM is not engaged seriously and there are fallacies of DM that weaken its efficiency. In this thesis, which is prepared in two sections, at the first section different techniques of DM are described and in the second section DM process is performed for a real world problem. For this purpose,...
SELM: Software engineering of machine learning models
, Article 20th International Conference on New Trends in Intelligent Software Methodologies, Tools and Techniques, SoMeT 2021, 21 September 2021 through 23 September 2021 ; Volume 337 , 2021 , Pages 48-54 ; 09226389 (ISSN); 9781643681948 (ISBN) ; Besharati, M. R ; Hourali, M ; Sharif University of Technology
IOS Press BV
2021
Abstract
One of the pillars of any machine learning model is its concepts. Using software engineering, we can engineer these concepts and then develop and expand them. In this article, we present a SELM framework for Software Engineering of machine Learning Models. We then evaluate this framework through a case study. Using the SELM framework, we can improve a machine learning process efficiency and provide more accuracy in learning with less processing hardware resources and a smaller training dataset. This issue highlights the importance of an interdisciplinary approach to machine learning. Therefore, in this article, we have provided interdisciplinary teams' proposals for machine learning. © 2021...
Graph-Based Outlier Detection
, M.Sc. Thesis Sharif University of Technology ; Daneshgar, Amir (Supervisor)
Abstract
One of the most heatedly debated issues in Computer Science is Outlier Detection due to its vast and substantial applications such as credit cards, Image Processing,tax fraud detection, and medical approaches. Consequently, Outlier detection has been researched within various domains and knowledge disciplines. On the other hand, the research attempts have not been sufficient to overcome this critical problem considerably inasmuch as nearly all proposed techniques are associated with a special kind of applications or datasets.Firstly, this thesis attempts to provide a precise definition which not only excludes other one’s drawbacks, but also has its distinctive merits. Three essential...
Outlier Censoring Based on Sparse Signal Recovery Algorithms
, M.Sc. Thesis Sharif University of Technology ; Karbasi, Mohammad (Supervisor)
Abstract
In today’s world, knowledge of the statistical behavior of noise can tremendously affect the accuracy of target detection in radar systems. Therefore, radar systems commonly collect a secondary dataset of homogeneous noise and estimate the statistics of the gathered data, prior to attempting target detection. Specifically, in the case of Gaussian noise with a mean of zero, the entire statistical information of the noise is encoded in its covariance matrix. In practice, however, the challenge is that the training samples do not purely contain homogeneous noise. In fact, some samples contain non-homogeneous outlier signals that do not have the same distribution as the noise samples. In this...