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multiclass-classification
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Multiclass Visual Object Recognition Based On Cluttered Images
, M.Sc. Thesis Sharif University of Technology ; Kasaei, Shohreh (Supervisor)
Abstract
With the advancement of Machine Vision and Image Processing systems, the need for conceptual interpretation is raising. For this interpretation, one should detect objects available in the image and then tries to find the relations between the objects. For a good interpretation of the image, the machine vision system should learn patterns available in the nature. Object recognition systems are also used in other vision tasks and they can be used for content-based image retrieval, control and surveillance, or human action and gesture recognition for a better and easier human-computer interface. In an object recognition system, first some features should be extracted from the input image and...
Multi-cass Semi-srvised Classification of Data Streams
, M.Sc. Thesis Sharif University of Technology ; Beigy, Hamid (Supervisor)
Abstract
Recent advances in storage and processing have provided the ability of automatic gathering of information which in turn leads to fast and contineous flow of data. The data which are produced and stored in this way are named data streams. It has many applications such as processing financial transactions, the recorded data of various sensors or the collected data by web sevices. Data streams are produced with high speed, large size and much dynamism and have some unique properties which make them applicable in precise modeling of many real data mining applications. The main challenge of data streams is the occurrence of concept drift which can be in four types: sudden, gradual, incremental or...
Predicting Imbalanced Data Using Machine Learning Approaches: a Case Study of Heart Patients
, M.Sc. Thesis Sharif University of Technology ; Khedmati, Majid (Supervisor)
Abstract
One of the challenging issues in machine learning is the problem of data imbalance. Data imbalance occurs when the number of samples from one or more classes significantly exceeds or falls behind that of other classes. The existence of data imbalance in a dataset often leads to misleading accuracy of models, inadequate prediction of minority class, and a lack of generalization. Data imbalance can be observed in various datasets such as fraud detection, disease diagnosis, email spam detection, and fake news detection. To address this issue, various methods have been proposed, categorized into four groups: data-level, algorithm-level, cost-sensitive, and ensemble approaches. In this study, two...
Active learning from positive and unlabeled data
, Article Proceedings - IEEE International Conference on Data Mining, ICDM, 11 December 2011 through 11 December 2011 ; December , 2011 , Pages 244-250 ; 15504786 (ISSN) ; 9780769544090 (ISBN) ; Rabiee, H. R ; Fadaee, M ; Manzuri, M. T ; Rohban, M. H ; Sharif University of Technology
2011
Abstract
During recent years, active learning has evolved into a popular paradigm for utilizing user's feedback to improve accuracy of learning algorithms. Active learning works by selecting the most informative sample among unlabeled data and querying the label of that point from user. Many different methods such as uncertainty sampling and minimum risk sampling have been utilized to select the most informative sample in active learning. Although many active learning algorithms have been proposed so far, most of them work with binary or multi-class classification problems and therefore can not be applied to problems in which only samples from one class as well as a set of unlabeled data are...
A Data-Driven Framework Based on Credit Risk Management for Improving the Performance of Peer-To-Peer Lending Platforms
, M.Sc. Thesis Sharif University of Technology ; Eshghi, Kourosh (Supervisor)
Abstract
Peoples need financial resources to improve their quality of life, and companies need them to progress and increase their comparative advantage. Borrowing is one of the most popular methods of financing. Peer-to-peer lending, also known as "marketplace" or "social" lending, is a novel form of intermediation that expunges the role of financial institutions (like banks) as an intermediary and allows entities (individuals or businesses) to raise loans directly from other entities. This lending platform can provide better quality services to borrowers (by quickly providing a simple loan application process through a transparent and flexible portal) and lenders (by managing their funding and live...
Active one-class learning by kernel density estimation
, Article IEEE International Workshop on Machine Learning for Signal Processing, 18 September 2011 through 21 September 2011 ; Septembe , 2011 , Page(s): 1 - 6 ; 9781457716232 (ISBN) ; Manzuri, M. T ; Rabiee, H. R ; Rohban, M. H ; Haghiri, S ; Sharif University of Technology
Abstract
Active learning has been a popular area of research in recent years. It can be used to improve the performance of learning tasks by asking the labels of unlabeled data from the user. In these methods, the goal is to achieve the highest possible accuracy gain while posing minimum queries to the user. The existing approaches for active learning have been mostly applicable to the traditional binary or multi-class classification problems. However, in many real-world situations, we encounter problems in which we have access only to samples of one class. These problems are known as one-class learning or outlier detection problems and the User relevance feedback in image retrieval systems is an...
Multiclass classification of patients during different stages of Alzheimer's disease using fMRI time-series
, Article Biomedical Physics and Engineering Express ; Volume 6, Issue 5 , 2020 ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
IOP Publishing Ltd
2020
Abstract
Alzheimer's Disease (AD) begins several years before the symptoms develop. It starts with Mild Cognitive Impairment (MCI) which can be separated into Early MCI and Late MCI (EMCI and LMCI). Functional connectivity analysis and classification are done among the different stages of illness with Functional Magnetic Resonance Imaging (fMRI). In this study, in addition to the four stages including healthy, EMCI, LMCI, and AD, the patients have been tracked for a year. Indeed, the classification has been done among 7 groups to analyze the functional connectivity changes in one year in different stages. After generating the functional connectivity graphs for eliminating the weak links, three...
RA-GCN: Graph convolutional network for disease prediction problems with imbalanced data
, Article Medical Image Analysis ; Volume 75 , 2022 ; 13618415 (ISSN) ; Kazi, A ; Soleymani Baghshah, M ; Rabiee, H. R ; Navab, N ; Sharif University of Technology
Elsevier B.V
2022
Abstract
Disease prediction is a well-known classification problem in medical applications. Graph Convolutional Networks (GCNs) provide a powerful tool for analyzing the patients’ features relative to each other. This can be achieved by modeling the problem as a graph node classification task, where each node is a patient. Due to the nature of such medical datasets, class imbalance is a prevalent issue in the field of disease prediction, where the distribution of classes is skewed. When the class imbalance is present in the data, the existing graph-based classifiers tend to be biased towards the major class(es) and neglect the samples in the minor class(es). On the other hand, the correct diagnosis...