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Semi-Supervised Learning on Single-View datasets by Integration of Multiple Co-trained Classifiers

Semi-Supervised Learning on Single-View Datasets by Integrat...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Jelena Slivka Ping Zhang Aleksandar Kovacevic Zora Konjovic Zoran Obradovic Computing and Control Department University of Novi Sad Serbia Center for Data Analytics and Biomedical Informatics Temple University Philadelnhia PA USA

We propose a novel semi-supervised learning algorithm, called IMCC, designed for co-training classifiers on single-view datasets. Our method runs the co-training algorithm for a predefined number of times, each time using a different random split of features. Thus, a set of diverse co-training classifiers is created. Each of these classifiers then labels each of the examples for which we want to determine the class label. In this way, each example for classification is assigned multiple labels. We then treat this as a problem of learning from inconsistent and unreliable annotators in a multi-annotator problem setting and estimate the single hidden true label for each example. In experimental results obtained on 25 benchmark datasets of various properties IMCC outperformed five considered alternative methods for co-training on single-view datasets, and resulted in a statistical tie with a Naive Bayes classifier trained using a much larger set of labeled examples.

关键词： Training Classification algorithms Accuracy Estimation Semisupervised learning Algorithm design and analysis Sensitivity and specificity

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Early classification of multivariate time series using a hybrid HMM/SVM model

Early classification of multivariate time series using a hyb...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mohamed F. Ghalwash Dušan Ramljak Zoran Obradović Center for Data Analytics and Biomedical Informatics Temple University Philadelphia USA Mathematics Department Faculty of Science Ain Shams University Cairo Egypt

Early classification of time series has been receiving a lot of attention as of late, particularly in the context of gene expression. In the biomédical realm, early classification can be of tremendous help, by identifying the onset of a disease before it has time to fully take hold, or determining that a treatment has done its job and can be discontinued. In this paper we present a state-of-the-art model, which we call the Early Classification Model (ECM), that allows for early, accurate, and patient-specific classification of multivariate time series. The model is comprised of an integration of the widely-used HMM and SVM models, which, while not a new technique per se, has not been used for early classification of multivariate time series classification until now. It attained very promising results on the datasets we tested it on: in our experiments based on a published dataset of response to drug therapy in Multiple Sclerosis patients, ECM used only an average of 40% of a time series and was able to outperform some of the baseline models, which needed the full time series for classification.

关键词： Hidden Markov models Time series analysis Gene expression Support vector machines Accuracy Training data models

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A method for design of data-tailored partitioning algorithms for optimizing the number of clusters in microarray analysis

A method for design of data-tailored partitioning algorithms...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Milan Vukicevic Boris Delibasic Milos Jovanovic Milija Suknovic Zoran Obradovic Center for Business Decision-Making Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Center for Data Analytics and Biomedical Informatics CIST Temple University Philadelphia USA

We propose a method for designing a partitioning clustering algorithm from reusable components that is suitable for finding the appropriate number of clusters (K) in microarray data. The proposed method is evaluated on 10 datasets (4 syntetic and 6 real-word microarrays) by considering 1008 reusable-component-based algorithms and four normalization methods. The best performing algorithm were reported on every dataset and also rules were identified for designing microarray-specific clustering algorithms. The obtained results indicate that in the majority of cases a data-tailored clustering algorithm design outperforms the results reported in the literature. In addition, data normalization can have an important influence on algorithm performance. The method proposed in this paper gives insights for design of divisive clustering algorithms that can reveal the optimal K in a microarray dataset.

关键词： Clustering algorithms Algorithm design and analysis Partitioning algorithms Indexes Gene expression Principal component analysis

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SPATIALLY PENALIZED REGRESSION FOR DEPENDENCE ANALYSIS OF RARE EVENTS: A STUDY IN PRECIPITATION EXTREMES

SPATIALLY PENALIZED REGRESSION FOR DEPENDENCE ANALYSIS OF RA...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Debasish Das Auroop Ganguly Snigdhansu Chatterjee Vipin Kumar Zoran Obradovic Center for Data Analytics and Biomedical Informatics Temple University Civil and Environmental Engineering Northeastern University School of Statistics University of Minnesota Computer Science and Engineering University of Minnesota

ISBN: (纸本)9781467311601

Discovery of dependence structure between precipitation extremes and other climate variables (covariates) within a smaller spatial and temporal neighborhood is an important step in better understanding the drivers of this complex phenomenon as well as short-term prediction of extremes occurrence. Apart from the inherent spatio-temporal variability of the dependence, it is further complicated by the availability of the covariates at different vertical levels. The above problem can be split into three different sub-problems. Firstly, a spatio-temporal neighborhood of influence has to be discovered, which can be different for different locations. Secondly, the dependence structure between the precipitation extremes and the covariates has to be discovered within this neighborhood and thirdly, it has to be investigated whether this dependence structure can be exploited for any predictive power. Climate scientists have already discovered some physics-based relations between some of the covariates (e.g. temperature, relative humidity, precipitable water etc.) and precipitation extremes. We are exploring data-dependent alternatives for these problems and any possibility of incorporating the physics-based relations into the resulting data model. In particular, we used elastic net-based sparse optimization technique which solves all three problems of neighborhood discovery, covariate dependence discovery and predictive modeling and at the same time maintains the interpretability of the resulting model. Preliminary results look promising and show potential for some interesting knowledge discovery. We are currently exploring non-linear correlations and the alternatives to combine the physics-based relationships into the data model.

关键词： One Two Three Four Five data models precipitation Chemical Precipitation precipitation (meteorology) extremum values Neighborhood DEPENDENCE data mining interpretability

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Online robust image alignment via iterative convex optimization

Online robust image alignment via iterative convex optimizat...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yi Wu Bin Shen Haibin Ling Center for Data Analytics & Biomedical Informatics Computer & Information Science Department Temple University Philadelphia PA USA Department of Computer Science Purdue University West Lafayette IN USA

In this paper we study the problem of online aligning a newly arrived image to previously well-aligned images. Inspired by recent advances in batch image alignment using low rank decomposition [16], we treat the newly arrived image, after alignment, as being linearly and sparsely reconstructed by the well-aligned ones. The task is accomplished by a sequence of convex optimization that minimizes the l\-norm. After that, online basis updating is pursued in two different ways: (1) a two-stage incremental alignment for joint registration of a large image dataset which is known a prior, and (2) a greedy online alignment of dynamically increasing image sequences, such as in the tracking scenario. In (1), we first sequentially collect basis images that are easily aligned by checking their reconstruction residuals, followed by the second stage where all images are re-aligned one-by-one using the collected basis set. In (2), during the tracking process, we dynamically enrich the image basis set by the new target if it significantly distinguishes itself from existing basis images. While inheriting the benefits of sparsity, our method enjoys the great time efficiency and therefore be capable of dealing with large image set and real time tasks such as visual tracking. The efficacy of the proposed online robust alignment algorithm is verified with extensive experiments on image set alignment and visual tracking, in reference with state-of-the-art methods.

关键词： Target tracking Visualization Face Robustness Joints Image reconstruction Convex functions

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Combining gene expression and function in a spatially localized approach

Combining gene expression and function in a spatially locali...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Evangelia I. Zacharaki Angeliki Skoura Desmond J. Smith Scott H. Faro Li An Vasileios Megalooikonomou Department of Computer Engineering and Informatics University of Patras Patras Greece Data Engineering Laboratory Center for Data Analytics and Biomedical Informatics Temple University Philadelphia USA Department of Molecular and Medical Pharmacology David Geffen School of Medicine UCLA Los Angeles USA Department of Radiology Temple University School of Medicine Philadelphia USA

The integration of gene expression datasets with gene function information provides valuable insights in unraveling the molecular mechanisms of the brain. In this paper, gene expression maps, acquired by the technique of voxelation, are analyzed using an atlas-based framework and the extracted spatial information is employed to organize genes in significant clusters. Gene function enrichment analysis of clusters enabled exploration of the relationships among brain regions, gene expression and gene function. In addition, the analysis revealed new function categories, for example biological process for the gene Pbx3, cellular component and biological process for the gene Ppplrlb. Our work confirms the hypothesis that genes of similar spatial expression patterns display similar functions indicating that our methodology could assist in the function identification of unannotated genes.

关键词： Gene expression Biological processes Educational institutions Ontologies Mice Prototypes Focusing

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Exploring genetic variability in drug therapy by selecting a minimum subset of the most informative single nucleotide polymorphisms through approximation of a markov blanket in a kernel-induced space

Exploring genetic variability in drug therapy by selecting a...

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IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology (CIBCB)

作者： Qiang Lou Henry P. Parkman Michael R. Jacobs Evgeny Krynetskiy Zoran Obradovic Center for Data Analytics and Biomedical Informatics Temple University Philadelphia USA Gastroenterology Section School of Medicine Temple University Philadelphia USA School of Pharmacy Temple University Philadelphia USA School of Pharmacy Jayne Haines Center for Pharmacogenomics and Drug Safety Temple University Philadelphia USA

Genome-wide analysis of single nucleotide polymorphisms (SNP) can potentially be helpful in exploring the role of genetic variability in drug therapy. However, two major problems with such an analysis are the need for a large number of interrogated genomes, and the resulting high-dimensional data where the number of SNPs used as features is much larger than the number of subjects. The aim of this study is to identify informative SNPs associated with clinical efficacy and side effects of domperidone treatment for gastroparesis from DNA microarray experiments by applying our feature selection method, which approximates the Markov Blanket in a kernel-induced space. DNA samples extracted from the saliva of 46 patients treated with domperidone were analyzed using Affymetrix 6.0 SNP microarrays. Experimental evaluations on this SNP microarray dataset provide evidence that our feature selection method can remove useless SNP features more accurately than existing Markov Blanket based alternatives.

关键词： Markov processes Kernel Approximation methods Approximation algorithms Accuracy Drugs Genomics

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Constraint graphs as security filters for privacy assurance in medical transactions 11

Constraint graphs as security filters for privacy assurance ...

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2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, ACM-BCB 2011

作者： Mathew, George Obradovic, Zoran Center for Data Analytics and Biomedical Informatics Temple University Philadelphia PA United States

ISBN: (纸本)9781450307963

We model transactions as exchange of graphs and propose constraint graphs as transaction filters for attribute-based transformations in clinical settings. An informal representation and working of constraint graphs is presented. A workbench was developed using a real world EMR system to demonstrate the concept. Results of our experiments using constraint graphs are consistent with published benchmark results. Copyright © 2011 ACM.

关键词： Medical informatics

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Learning-based automatic breast tumor detection and segmentation in ultrasound images

Learning-based automatic breast tumor detection and segmenta...

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IEEE International Symposium on biomedical Imaging

作者： Peng Jiang Jingliang Peng Guoquan Zhang Erkang Cheng Vasileios Megalooikonomou Haibin Ling School of Computer Science and Technology Shandong Provincial Key Laboratory of Software Engineering P.R.China Department of Ultrasound Shandong Provincial Hospital Affiliated to Shandong University P.R. China Center for Data Analytics & Biomedical Informatics Temple University Philadelphia PA USA

ISBN: (纸本)9781457718571

Ultrasound (US) images have been widely used in the diagnosis of breast cancer in particular. While experienced doctors may locate the tumor regions in a US image manually, it is highly desirable to develop algorithms that automatically detect the tumor regions in order to assist medical diagnosis. In this paper, we propose a novel algorithm for automatic detection of breast tumors in US images. We formulate the tumor detection as a two step learning problem: tumor localization by bounding box and exact boundary delineation. Specifically, the proposed method uses an AdaBoost classifier on Harr-like features to detect a preliminary set of tumor regions. The preliminarily detected tumor regions are further screened with a support vector machine using quantized intensity features. Finally, the random walk segmentation algorithm is performed on the US image to retrieve the boundary of each detected tumor region. The proposed method has been evaluated on a data set containing 112 breast US images, including histologically confirmed 80 diseased ones and 32 normal ones. The data set contains one image from each patient and the patients are from 31 to 75 years old. Experiments demonstrate that the proposed algorithm can automatically detect breast tumors, with their locations and boundary shapes retrieved with high accuracy.

关键词： Image segmentation Support vector machines Breast tumors Ultrasonic imaging Feature extraction Accuracy

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Imputation of missing links and attributes in longitudinal social surveys

Imputation of missing links and attributes in longitudinal s...

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11th IEEE International Conference on data Mining Workshops, ICDMW 2011

作者： Ouzienko, Vladimir Obradovic, Zoran Center for Data Analytics and Biomedical Informatics Temple University Philadelphia PA 19122 United States

ISBN: (纸本)9780769544090

We propose a unified approach for imputation of the links and attributes in longitudinal social surveys which accounts for changing network topology and interdependence between the actor's links and attributes. The previous studies on the treatment of non-respondents in longitudinal social networks were mostly concerned with imputation of the missing links only or imputation effects on the networks statistics. For this study we conduct a set of experiments on synthetic and real life datasets with 20%-60% of nodes missing under four mechanisms. The obtained results were better than when using alternative methods which suggest that our method can be used as a viable imputation tool. © 2011 IEEE.

关键词： Network topology

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