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Supervised clustering of label ranking data

Supervised clustering of label ranking data

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12th SIAM International Conference on data Mining, SDM 2012

作者： Grbovic, Mihajlo Djuric, Nemanja Vucetic, Slobodan Department of Computer and Information Sciences Center for Data Analytics and Biomedical Informatics Temple University United States

ISBN: (纸本)9781611972320

In this paper we study supervised clustering in the context of label ranking data. Segmentation of such complex data has many potential real-world applications. For example, in target marketing, the goal is to cluster customers in the feature space by taking into consideration the assigned, potentially incomplete product preferences, such that the preferences of instances within a cluster are more similar than the preferences of customers in the other clusters. We establish several heuristic baselines for this application that make use of well-known algorithms such as K-means, and propose a principled algorithm specifically tailored for this type of clustering. It is based on the Plackett- Luce (PL) probabilistic ranking model. Each cluster is represented as a union of Voronoi cells defined by a set of prototypes and is assigned a set of PL label scores that determine the cluster-specific label ranking. The unknown cluster PL parameters and prototype positions are determined using a supervised learning technique. Cluster membership and ranking for a new instance is determined by membership of its nearest prototype. The proposed algorithms were empirically evaluated on synthetic and reallife label ranking data. The PL-based method was superior to the heuristically-based supervised clustering approaches. The proposed PL-based algorithm was also evaluated on the task of label ranking prediction. The results showed that it is highly competitive to the state of the art label ranking algorithms, and that it is particularly accurate on data with partial rankings. Copyright © 2012 by the Society for Industrial and Applied Mathematics.

关键词： Supervised learning

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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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2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012

作者： Vukicevic, Milan Delibasic, Boris Jovanovic, Milos Suknovic, Milija Obradovic, Zoran 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 United States

ISBN: (纸本)9781467311892

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. © 2012 IEEE.

关键词： Clustering algorithms

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A data mining approach for optimization of acute inflammation therapy

A data mining approach for optimization of acute inflammatio...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Vladan Radosavljević Kosta Ristovski Zoran Obradović Center for Data Analytics and Biomedical Informatics Temple University Philadelphia USA

Acute inflammation is a medical condition which occurs over seconds, minutes or hours and is characterized as a systemic inflammatory response to an infection. Delaying treatment by only one hour decreases patient chance of survival by about 7%. Therefore, there is a critical need for tools that can aid therapy optimization for this potentially fatal condition. Towards this objective we developed a data driven approach for therapy optimization where a predictive model for patients' behavior is learned directly from historical data. As such, the predictive model is incorporated into a model predictive control optimization algorithm to find optimal therapy, which will lead the patient to a healthy state. To save on the cost of clinical trials and potential failure, we evaluated our model on a population of virtual patients capable of emulating the inflammatory response. Patients are treated with two drugs for which dosage and timing are critical for the outcome of the treatment. Our results show significant improvement in percentage of healthy outcomes comparing to previously proposed methods for acute inflammation treatment found in literature and in clinical practice. In particular, application of our method rescued 88% of patients that would otherwise die within 168 hours due to septic or aseptic state. In contrast, the best method from literature rescued only 73% of patients.

关键词： Predictive models Medical treatment Mathematical model Optimization Predictive control data models Pathogens

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Predicting viral infection by selecting informative biomarkers from temporal high-dimensional gene expression data

Predicting viral infection by selecting informative biomarke...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Qiang Lou Zoran Obradovic Center for Data Analytics and Biomedical Informatics Temple University Philadelphia USA

In order to more accurately predict an individual's health status, in clinical applications it is often important to perform analysis of high-dimensional gene expression data that varies with time. A major challenge in predicting from such temporal microarray data is that the number of biomarkers used as features is typically much larger than the number of labeled subjects. One way to address this challenge is to perform feature selection as a preprocessing step and then apply a classification method on selected features. However, traditional feature selection methods cannot handle multivariate temporal data without applying techniques that flatten temporal data into a single matrix in advance. In this study, a feature selection filter that can directly select informative features from temporal gene expression data is proposed. In our approach we measure the distance between multivariate temporal data from two subjects. Based on this distance, we define the objective function of temporal margin based feature selection to maximize each subject's temporal margin in its own relevant subspace. The experimental results on two real flu data sets provide evidence that our method outperforms the alternatives, which flatten the temporal data in advance.

关键词： Gene expression Biomarkers Time measurement Time series analysis Optimization Predictive models Vectors

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Distributed Privacy Preserving Decision Support System for Predicting Hospitalization Risk in Hospitals with Insufficient data

Distributed Privacy Preserving Decision Support System for P...

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International Conference on Machine Learning and Applications (ICMLA)

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

Building prediction models for suggestive knowledge from multiple sources dynamically is of great interest from a clinical decision support point of view. This is valuable in situations where the local clinical data repository does not have sufficient number of records to draw conclusions from. However, due to privacy concerns, hospitals are reluctant to divulge patient records. Consequently, a distributed model building mechanism that can use just the statistics from multiple hospitals' databases is valuable. Our DIDT algorithm builds a model in that fashion. In this study, using National Inpatient Sample (NIS) data for 2009, we demonstrate that DIDT algorithm can be used to help collaboratively build a better decision-making model in situations where hospitals have small number of records that are insufficient to make good local models. Based on 262 attributes used for model building, we showed that 9 collaborating hospitals each with less than 100 cases of hospitalizations related to diabetes were able to achieve 9.9% improvement in accuracies of hospitalization prediction collectively using a distributed model as compared to relying on local models developed on their own. When relying on local risk prediction models for diabetes at these 9 hospitals, 159 of 357 patients were misclassified and prediction was impossible for another 16 patients. Our integrated model reduced the misclassification to 138 effectively providing accurate early diagnostics to 37 additional patients. We also introduce the concept of banding to improve DIDT algorithm so as to logically combine multiple hospitals when large number of hospitals is involved for reduction in cross-validation folds.

关键词： Hospitals Accuracy Distributed databases data models Buildings Predictive models Diabetes

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Using an atlas-based approach in the analysis of gene expression maps obtained by voxelation

Using an atlas-based approach in the analysis of gene expres...

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8th International Workshop on Artificial Intelligence Applications and Innovations, AIAI 2012: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB

作者： Zacharaki, Evangelia I. Skoura, Angeliki An, Li Smith, Desmond J. Megalooikonomou, Vasileios Department of Computer Engineering and Informatics University of Patras Patras Greece Data Engineering Laboratory Center for Data Analytics and Biomedical Informatics Temple University PA United States Department of Molecular and Medical Pharmacology David Geffen School of Medicine UCLA CA United States

ISBN: (纸本)9783642334115

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. Moreover, gene function enrichment analysis of clusters enables exploring the relationships among brain regions, gene expressions and gene functions. Our work confirms the hypothesis that genes of similar spatial expression patterns display similar functions indicating that our methodology could assist in the functional identification of unannotated genes. © 2012 IFIP International Federation for Information Processing.

关键词： Brain mapping

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Integration of multiple annotators by aggregating experts and filtering novices

Integration of multiple annotators by aggregating experts an...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ping Zhang Zoran Obradovic Center for Data Analytics and Biomedical Informatics Temple University Philadelphia PA USA

Learning from noisy labels obtained from multiple annotators and without access to any true labels is an increasingly important problem in bioinformatics and biomedicine. In our method, this challenge is addressed by iteratively filtering low-quality annotators and estimating the consensus labels based only on the remaining experts that provide higher-quality annotations. Experiments on biomedical text classification and CASP9 protein disorder prediction tasks provide evidence that the proposed algorithm is more accurate than the majority voting and previously developed multi-annotator approaches. The benefit of using the new method is particularly large when low-quality annotators dominate. Moreover, the new algorithm also suggests the most relevant annotators for each instance, thus paving the way for understanding the behaviors of each annotator and building more reliable predictive models for bioinformatics applications.

关键词： Estimation Classification algorithms Filtering Accuracy Sensitivity Noise measurement Proteins

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Towards understanding dominant processes in complex dynamical systems: Case of precipitation extremes

Towards understanding dominant processes in complex dynamica...

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6th International Workshop on Knowledge Discovery from Sensor data, SensorKDD'12 - Held in Conjunction with the 18th ACM SIGKDD Conference on Knowledge Discovery and data Mining, KDD 2012

作者： Das, Debasish Ganguly, Auroop Banerjee, Arindam Obradovic, Zoran Department of Civil and Environmental Engineering Northeastern University Boston MA United States Center for Data Analytics and Biomedical Informatics Temple University Philadelphia PA United States Computer Science and Engineering University of Minnesota Minneapolis MN United States

ISBN: (纸本)9781450315548

Complex dynamical systems like precipitation extremes under climate variability or change are typically governed by multiple processes at multiple scales. The processes themselves may be manifested at multiple scales and would need to be captured through key indicator variables, which in turn may be better projected by physical models than the variables of interest. We posit that hybrid approaches based on physically-motivated approaches and data-driven methods, which in turn are conditioned on both observations and simulations from large-scale physics-based models, may offer novel and quantifiable insights. The data-driven approaches may need to extend and adapt methods developed and tested in statistics, data mining and machine learning to the concept of dominant processes. In this paper, we performed some exploratory data analysis to characterize the effect of dominant processes on precipitation extremes, annually and seasonally, and from global and century scale to regional and decadal scale, and found some interesting insights that pointed towards need of improved understanding. We identified the gaps in understanding the regional drivers where data-driven methods can make useful improvements and eventually lead to a predictive model for precipitation extremes. Although we do not propose any specific method for solving the problem, we realize that any successful data mining solution should include all or a subset of tools like dimensionality reduction, grouped variable selection, non-linear regression and graphical models. The concepts of dominant processes proposed here would likely generalize broadly to climate extremes while the solution frameworks themselves may generalize beyond climate. Copyright 2012 ACM.

关键词： data mining

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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...

引用

2012 IEEE Symposium on Computational Intelligence and Computational Biology, CIBCB 2012

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

ISBN: (纸本)9781467311892

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. © 2012 IEEE.

关键词： Feature Selection

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Towards a DGSS Prototype for Early Warning for Ski Injuries

Towards a DGSS Prototype for Early Warning for Ski Injuries

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International Conference on data Engineering Workshops

作者： Boris Delibaic Zoran Obradovic Center for Business Decision-Making University of Belgrade Belgrade Serbia Center for Data Analytics and Biomedical Informatics Temple University Philadelphia PA USA

This paper reports on our ongoing research in ski injury analysis. We propose a decision guidance support system for early warning for ski injuries. We report several injury patterns in form of IF-THEN rules which could be used to build a decision guidance support system for early warning for ski injuries. The rules are induced from data merged from two databases, namely the Mountaineer rescue service database and the Ski gate entrances database. Around 2000 injuries from year 2006 to year 2011 were analyzed. Based on movement of skiers (recorded through ski gates on each lift) several statistics for each accident (e.g. average speed of skier, average weight of slopes) have been calculated.

关键词： Injuries databases Accidents Snow Logic gates Medical diagnostic imaging

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