X-ray Astronomical Point Sources Recognition Using Granular Binary-tree SVM

The study on point sources in astronomical images is of special importance, since most energetic celestial objects in the Universe exhibit a point-like appearance. An approach to recognize the point sources (PS) in the X-ray astronomical images using our newly designed granular binary-tree support vector machine (GBT-SVM) classifier is proposed. First, all potential point sources are located by peak detection on the image. The image and spectral features of these potential point sources are then extracted. Finally, a classifier to recognize the true point sources is build through the extracted features. Experiments and applications of our approach on real X-ray astronomical images are demonstrated. comparisons between our approach and other SVM-based classifiers are also carried out by evaluating the precision and recall rates, which prove that our approach is better and achieves a higher accuracy of around 89%.Comment: Accepted by ICSP201

A novel method for mining highly imbalanced high-throughput screening data in PubChem

Motivation: The comprehensive information of small molecules and their biological activities in PubChem brings great opportunities for academic researchers. However, mining high-throughput screening (HTS) assay data remains a great challenge given the very large data volume and the highly imbalanced nature with only small number of active compounds compared to inactive compounds. Therefore, there is currently a need for better strategies to work with HTS assay data. Moreover, as luciferase-based HTS technology is frequently exploited in the assays deposited in PubChem, constructing a computational model to distinguish and filter out potential interference compounds for these assays is another motivation

Mitigating the effect of covariates in face recognition

Current face recognition systems capture faces of cooperative individuals in controlled environment as part of the face recognition process. It is therefore possible to control lighting, pose, background, and quality of images. However, in a real world application, we have to deal with both ideal and imperfect data. Performance of current face recognition systems is affected for such non-ideal and challenging cases. This research focuses on designing algorithms to mitigate the effect of covariates in face recognition.;To address the challenge of facial aging, an age transformation algorithm is proposed that registers two face images and minimizes the aging variations. Unlike the conventional method, the gallery face image is transformed with respect to the probe face image and facial features are extracted from the registered gallery and probe face images. The variations due to disguises cause change in visual perception, alter actual data, make pertinent facial information disappear, mask features to varying degrees, or introduce extraneous artifacts in the face image. To recognize face images with variations due to age progression and disguises, a granular face verification approach is designed which uses dynamic feed-forward neural architecture to extract 2D log polar Gabor phase features at different granularity levels. The granular levels provide non-disjoint spatial information which is combined using the proposed likelihood ratio based Support Vector Machine match score fusion algorithm. The face verification algorithm is validated using five face databases including the Notre Dame face database, FG-Net face database and three disguise face databases.;The information in visible spectrum images is compromised due to improper illumination whereas infrared images provide invariance to illumination and expression. A multispectral face image fusion algorithm is proposed to address the variations in illumination. The Support Vector Machine based image fusion algorithm learns the properties of the multispectral face images at different resolution and granularity levels to determine optimal information and combines them to generate a fused image. Experiments on the Equinox and Notre Dame multispectral face databases show that the proposed algorithm outperforms existing algorithms. We next propose a face mosaicing algorithm to address the challenge due to pose variations. The mosaicing algorithm generates a composite face image during enrollment using the evidence provided by frontal and semiprofile face images of an individual. Face mosaicing obviates the need to store multiple face templates representing multiple poses of a users face image. Experiments conducted on three different databases indicate that face mosaicing offers significant benefits by accounting for the pose variations that are commonly observed in face images.;Finally, the concept of online learning is introduced to address the problem of classifier re-training and update. A learning scheme for Support Vector Machine is designed to train the classifier in online mode. This enables the classifier to update the decision hyperplane in order to account for the newly enrolled subjects. On a heterogeneous near infrared face database, the case study using Principal Component Analysis and C2 feature algorithms shows that the proposed online classifier significantly improves the verification performance both in terms of accuracy and computational time

A Supervised Learning Approach for Imbalanced Text Classification of Biomedical Literature Triage

This thesis presents the development of a machine learning system, called mycoSORT , for supporting the first step of the biological literature manual curation process, called triage. The manual triage of documents is very demanding, as researchers usually face the time-consuming and error- prone task of screening a large amount of data to identify relevant information. After querying scientific databases for keywords related to a specific subject, researchers generally find a long list of retrieved results, that has to be carefully analysed to identify only a few documents that show a potential of being relevant to the topic. Such an analysis represents a severe bottleneck in the knowledge discovery and decision-making processes in scientific research. Hence, biocurators could greatly benefit from an automatic support when performing the triage task. In order to support the triage of scientific documents, we have used a corpus of document instances manually labeled by biocurators as “selected” or “rejected”, with regards to their potential to indicate relevant information about fungal enzymes. This document collection is characterized by being large, since many results are retrieved and analysed to finally identify potential candidate documents; and also highly imbalanced, concerning the distribution of instances per relevance: the great majority of documents are labeled as rejected, while only a very small portion are labeled as selected. Using this dataset, we studied the design of a classification model to identify the most discriminative features to automate the triage of scientific literature and to tackle the imbalance between the two classes of documents. To identify the most suitable model, we performed a study of 324 classification models, which demonstrated the results of using 9 different data undersampling factors, 4 sets of features, and the evaluation of 2 feature selection methods as well as 3 machine learning algorithms. Our results demonstrated that the use of an undersampling technique is effective to handle imbalanced datasets and also help manage large document collections. We also found that the combination of undersampling and feature selection using Odds Ratio can improve the performance of our classification model. Finally, our results demonstrated that the best fitting model to support the triage of scientific documents is composed by domain relevant features, filtered by Odds Ratio scores, the use of dataset undersampling and the Logistic Model Trees algorithm

Granular Support Vector Machines Based on Granular Computing, Soft Computing and Statistical Learning

With emergence of biomedical informatics, Web intelligence, and E-business, new challenges are coming for knowledge discovery and data mining modeling problems. In this dissertation work, a framework named Granular Support Vector Machines (GSVM) is proposed to systematically and formally combine statistical learning theory, granular computing theory and soft computing theory to address challenging predictive data modeling problems effectively and/or efficiently, with specific focus on binary classification problems. In general, GSVM works in 3 steps. Step 1 is granulation to build a sequence of information granules from the original dataset or from the original feature space. Step 2 is modeling Support Vector Machines (SVM) in some of these information granules when necessary. Finally, step 3 is aggregation to consolidate information in these granules at suitable abstract level. A good granulation method to find suitable granules is crucial for modeling a good GSVM. Under this framework, many different granulation algorithms including the GSVM-CMW (cumulative margin width) algorithm, the GSVM-AR (association rule mining) algorithm, a family of GSVM-RFE (recursive feature elimination) algorithms, the GSVM-DC (data cleaning) algorithm and the GSVM-RU (repetitive undersampling) algorithm are designed for binary classification problems with different characteristics. The empirical studies in biomedical domain and many other application domains demonstrate that the framework is promising. As a preliminary step, this dissertation work will be extended in the future to build a Granular Computing based Predictive Data Modeling framework (GrC-PDM) with which we can create hybrid adaptive intelligent data mining systems for high quality prediction

Developing and deploying data mining techniques in healthcare

Improving healthcare is a top priority for all nations. US healthcare expenditure was $3 trillion in 2014. In the same year, the share of GDP assigned to healthcare expenditure was 17.5%. These statistics shows the importance of making improvement in healthcare delivery system. In this research, we developed several data mining methods and algorithms to address healthcare problems. These methods can also be applied to the problems in other domains.The first part of this dissertation is about rare item problem in association analysis. This problem deals with the discovering rare rules, which include rare items. In this study, we introduced a novel assessment metric, called adjusted support to address this problem. By applying this metric, we can retrieve rare rules without over-generating association rules. We applied this method to perform association analysis on complications of diabetes.The second part of this dissertation is developing a clinical decision support system for predicting retinopathy. Retinopathy is the leading cause of vision loss among American adults. In this research, we analyzed data from more than 1.4 million diabetic patients and developed four sets of predictive models: basic, comorbid, over-sampled, and ensemble models. The results show that incorporating comorbidity data and oversampling improved the accuracy of prediction. In addition, we developed a novel "confidence margin" ensemble approach that outperformed the existing ensemble models. In ensemble models, we also addressed the issue of tie in voting-based ensemble models by comparing the confidence margins of the base predictors.The third part of this dissertation addresses the problem of imbalanced data learning, which is a major challenge in machine learning. While a standard machine learning technique could have a good performance on balanced datasets, when applied to imbalanced datasets its performance deteriorates dramatically. This poor performance is rather troublesome especially in detecting the minority class that usually is the class of interest. In this study, we proposed a synthetic informative minority over-sampling (SIMO) algorithm embedded into support vector machine. We applied SIMO to 15 publicly available benchmark datasets and assessed its performance in comparison with seven existing approaches. The results showed that SIMO outperformed all existing approaches

A model driven approach to imbalanced data learning

Ph.DDOCTOR OF PHILOSOPH