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IEEE International Conference on Bioinformatics and Biomedicine

作者： Mohammed A. Gaafar Noha A. Yousri Mohamed A. Ismail Computer and Systems Engineering Alexandria University Alexandria Egypt

ISBN: (纸本)9781479913114

Ensembles of classifiers were shown to provide better accuracy than single classifiers. However, the classification robustness is an important performance measure for classifiers and ensembles, besides accuracy, that should be considered. Increasing the robustness of classification systems results in reducing the probability of over-fitting. The robustness, as defined in this study, has not been studied in the literature. In this paper, a framework is used to prove that ensembles of classifiers are more robust than single classifiers. The framework selects different ensembles of classifiers and compares their robustness to the robustness of their members. The experiments performed on six different microarray datasets showed that ensembles of classifiers are more robust than their members.

关键词： Ensemble Selection microarray classification Cancer classification Classifiers Robustness Classifiers Ensembles cancer classification Robustness neoplasm diagnosis Performance metrics microarray

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Uncertainty estimation with a finite dataset in the assessment of classification models

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2012年第5期56卷 1016-1027页

作者： Chen, Weijie Yousef, Waleed A. Gallas, Brandon D. Hsu, Elizabeth R. Lababidi, Samir Tang, Rong Pennello, Gene A. Symmans, W. Fraser Pusztai, Lajos US FDA Div Imaging & Appl Math Off Sci & Engn Labs Ctr Devices & Radiol Hlth Silver Spring MD USA Helwan Univ Fac Comp & Informat Dept Comp Sci Helwan Egypt US FDA Div Biostat Off Surveillance & Biometr Ctr Devices & Radiol Hlth Silver Spring MD USA Univ Texas MD Anderson Canc Ctr Dept Breast Med Oncol Houston TX 77030 USA Univ Texas MD Anderson Canc Ctr Dept Pathol Houston TX 77030 USA

To successfully translate genomic classifiers to the clinical practice, it is essential to obtain reliable and reproducible measurement of the classifier performance. A point estimate of the classifier performance has to be accompanied with a measure of its uncertainty. In general, this uncertainty arises from both the finite size of the training set and the finite size of the testing set. The training variability is a measure of classifier stability and is particularly important when the training sample size is small. Methods have been developed for estimating such variability for the performance metric AUC (area under the ROC curve) under two paradigms: a smoothed cross-validation paradigm and an independent validation paradigm. The methodology is demonstrated on three clinical microarray datasets in the microarray quality control consortium phase two project (MAQC-II): breast cancer, multiple myeloma, and neuroblastoma. The results show that the classifier performance is associated with large variability and the estimated performance may change dramatically on different datasets. Moreover, the training variability is found to be of the same order as the testing variability for the datasets and models considered. In conclusion, the feasibility of quantifying both training and testing variability of classifier performance is demonstrated on finite real-world datasets. The large variability of the performance estimates shows that patient sample size is still the bottleneck of the microarray problem and the training variability is not negligible. Published by Elsevier B.V.

关键词： Uncertainty Training variability microarray classification Area under the ROC curve (AUC)

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A novel ensemble of classifiers for microarray data classification

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APPLIED SOFT COMPUTING 2008年第4期8卷 1664-1669页

作者： Chen, Yuehui Zhao, Yaou Univ Jinan Sch Informat Sci & Engn Computat Intelligence Lab Jinan 250022 Peoples R China

Micorarray data are often extremely asymmetric in dimensionality, such as thousands or even tens of thousands of genes and a few hundreds of samples. Such extreme asymmetry between the dimensionality of genes and samples presents several challenges to conventional clustering and classification methods. In this paper, a novel ensemble method is proposed. Firstly, in order to extract useful features and reduce dimensionality, different feature selection methods such as correlation analysis, Fisher-ratio is used to form different feature subsets. Then a pool of candidate base classifiers is generated to learn the subsets which are re-sampling from the different feature subsets with PSO (Particle Swarm Optimization) algorithm. At last, appropriate classifiers are selected to construct the classification committee using EDAs (Estimation of Distribution Algorithms). Experiments show that the proposed method produces the best recognition rates on four benchmark databases. (c) 2008 Elsevier B.V. All rights reserved.

关键词： microarray classification estimation of distribution algorithms (EDA) particle swarm optimization (PSO) ensemble learning correlation analysis Fisher-ratio

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A Novel Ensemble Selection Method for Cancer Diagnosis Using microarray Datasets

A Novel Ensemble Selection Method for Cancer Diagnosis Using...

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12th IEEE International Conference on BioInformatics and BioEngineering (BIBE)

作者： Gaafar, Mohammed A. Yousri, Noha A. Ismail, Mohamed A. Univ Alexandria Alexandria Egypt

ISBN: (纸本)9781467343589;9781467343572

Ensembles of classifiers have recently proved their efficiency in cancer diagnosis based on microarray datasets. The main performance indicators, namely, accuracy and diversity, present the main focus of study when designing an ensemble. One other important performance indicator is classification robustness. In an attempt to improve the performance of an ensemble, the proposed algorithm presents a variation concerning the diversity method used. The proposed algorithm attempts to enhance the robustness of the classification by searching for an ensemble of diverse classifiers. Also, a comparison of the different diversity methods is presented in order to study their impact on the robustness of the classification. The experiments performed show that the diversity method used in the proposed algorithm outperforms the other diversity methods in terms of accuracy and robustness.

关键词： Gene Selection Ensemble Selection microarray classification Cancer classification

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Multiple kernel learning based on local and nonlinear combinations

Multiple kernel learning based on local and nonlinear combin...

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38th Latin America Conference on Informatics (CLEI)

作者： Calderon-Niquin, Marks Valverde-Rebaza, Jorge Univ Nacl Trujillo Dept Informat Trujillo Peru Univ Sao Paulo Inst Ciencias Matemat & Comp BR-13560970 Sao Carlos SP Brazil

ISBN: (纸本)9781467307932;9781467307949

In recent years, different methods based on kernels have been used with success in a variety of tasks such as classification. However, in the typical use of these methods, the choice of the optimal kernel is crucial to improve the performance of a specific task. So, instead of selecting a single kernel, multiple kernel learning (MKL) has been proposed which uses a combination of kernels, where the weight of each kernel is optimized in the training stage. MKL methods use kernels in linear, nonlinear or data-dependent combinations. Methods based on MKL have performed better than methods using a single kernel such as the Support Vector Machine (SVM). In this article, we propose a new MKL method, which is based on a local (data dependent) and nonlinear combination of different kernels using a gating model for selecting the appropriate kernel function. We call our proposal as localized nonlinear multiple kernel learning (LNLMKL). In our experiments for binary microarray classification, different kernels were used in SVM and different kernels combinations were used for our proposal and for the other MKL methods. Finally, we report the results of these experiments using eight high-dimensional microarray data sets demonstrating that our proposal have performed better than the other methods analyzed.

关键词： support vector machines kernel machines microarrays multiple kernel learning microarray classification

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GENE EXPRESSION DATA classification COMBINING HIERARCHICAL REPRESENTATION AND EFFICIENT FEATURE SELECTION

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JOURNAL OF BIOLOGICAL SYSTEMS 2012年第4期20卷 349-375页

作者： Bosio, Mattia Bellot, Pau Salembier, Philippe Oliveras-Verges, Albert Tech Univ Catalonia UPC Dept Signal Theory & Commun Barcelona 08034 Spain

A general framework for microarray data classification is proposed in this paper. It produces precise and reliable classifiers through a two-step approach. At first, the original feature set is enhanced by a new set of features called metagenes. These new features are obtained through a hierarchical clustering process on the original data. Two different metagene generation rules have been analyzed, called Treelets clustering and Euclidean clustering. Metagenes creation is attractive for several reasons: first, they can improve the classification since they broaden the available feature space and capture the common behavior of similar genes reducing the residual measurement noise. Furthermore, by analyzing some of the chosen metagenes for classification with gene set enrichment analysis algorithms, it is shown how metagenes can summarize the behavior of functionally related probe sets. Additionally, metagenes can point out, still undocumented, highly discriminant probe sets numerically related to other probes endowed with prior biological information in order to contribute to the knowledge discovery process. The second step of the framework is the feature selection which applies the Improved Sequential Floating Forward Selection algorithm (IFFS) to properly choose a subset from the available feature set for classification composed of genes and metagenes. Considering the microarray sample scarcity problem, besides the classical error rate, a reliability measure is introduced to improve the feature selection process. Different scoring schemes are studied to choose the best one using both error rate and reliability. The Linear Discriminant Analysis classifier (LDA) has been used throughout this work, due to its good characteristics, but the proposed framework can be used with almost any classifier. The potential of the proposed framework has been evaluated analyzing all the publicly available datasets offered by the Micro Array Quality Control Study, phase II (MAQC). Th

关键词： microarray classification metagenes hierarchical representation Treelets feature selection LDA

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Two-group classification with high-dimensional correlated data: A factor model approach

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2011年第11期55卷 2975-2990页

作者： Pedro Duarte Silva, A. Catolica Portuguesa Porto Fac Econ & Gestao P-4169005 Oporto Portugal Catolica Portuguesa Porto CEGE P-4169005 Oporto Portugal

A class of linear classification rules, specifically designed for high-dimensional problems, is proposed. The new rules are based on Gaussian factor models and are able to incorporate successfully the information contained in the sample correlations. Asymptotic results, that allow the number of variables to grow faster than the number of observations, demonstrate that the worst possible expected error rate of the proposed rules converges to the error of the optimal Bayes rule when the postulated model is true, and to a slightly larger constant when this model is a reasonable approximation to the data generating process. Numerical comparisons suggest that, when combined with appropriate variable selection strategies, rules derived from one-factor models perform comparably, or better, than the most successful extant alternatives under the conditions they were designed for. The proposed methods are implemented as an R package named HiDimDA, available from the CRAN repository. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Discriminant Analysis High dimensionality Expected misclassification rates microarray classification

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On the Comparison of Classifiers for microarray Data

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CURRENT BIOINFORMATICS 2010年第1期5卷 29-39页

作者： Hanczar, Blaise Dougherty, Edward R. Univ Paris 05 LIPADE F-75006 Paris France Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Translat Genom Res Inst Computat Biol Div Phoenix AZ 85004 USA

The aim of many microarray experiments is to build discriminatory diagnosis and prognosis models. A large number of supervised methods have been proposed in literature for microarray-based classification. Model comparison, which is based on the classification error estimation, is a critical issue. Previous studies have shown that error estimation is unreliable in high-dimensional small-sample settings. This leads naturally to questioning the validity of classification-rule comparison approaches being used in the literature. In this paper we present a brief review of the different comparison methods used in bioinformatics. Then, we test these methods on a set of simulations based on both synthetic and real data. These simulations include different feature-label distributions, classification rules, error estimators and variance estimators. The results show that none of these methods can provide reliable comparison across a wide spectrum of feature-label distributions and classification rules.

关键词： microarray classification error estimation classifier comparison variance study

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Variable selection via combined penalization for high-dimensional data analysis

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COMPUTATIONAL STATISTICS & DATA ANALYSIS 2010年第10期54卷 2230-2243页

作者： Wang, Xiaoming Park, Taesung Carriere, K. C. Univ Alberta Dept Math & Stat Sci Edmonton AB T6G 2G1 Canada Shanghai Univ Finance & Econ Sch Stat & Management Shanghai 200433 Peoples R China Seoul Natl Univ Dept Stat Seoul South Korea

We propose a new penalized least squares approach to handling high-dimensional statistical analysis problems. Our proposed procedure can outperform the SCAD penalty technique (Fan and Li, 2001) when the number of predictors p is much larger than the number of observations n, and/or when the correlation among predictors is high. The proposed procedure has some of the properties of the smoothly clipped absolute deviation (SCAD) penalty method, including sparsity and continuity, and is asymptotically equivalent to an oracle estimator. We show how the approach can be used to analyze high-dimensional data, e.g., microarray data, to construct a classification rule and at the same time automatically select significant genes. A simulation study and real data examples demonstrate the practical aspects of the new method. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Linear model Combined penalization SCAD Ridge penalty GCV Variable selection microarray classification

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Gene Selection of Multiple Cancer Types via Huberized Multi-class Support Vector Machine

Gene Selection of Multiple Cancer Types via Huberized Multi-...

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Joint 48th IEEE Conference on Decision and Control (CDC) / 28th Chinese Control Conference (CCC)

作者： Li, Juntao Jia, Yingmin Du, Junping Yu, Fashan Beihang Univ BUAA Dept Syst & Control Beijing 100191 Peoples R China

ISBN: (纸本)9781424438723

This paper proposes a new type of regularization in the context of multi-class support vector machine for simultaneous classification and gene selection. By combining the huberized hinge loss function and the elastic net penalty, the proposed support vector machine can do automatic gene selection and further encourage a grouping effect in the process of building classifiers, thus leading a sparse multi-classifiers with enhanced interpretability. Furthermore, a reasonable correlation between the two regularization parameters is proposed and an efficient solution path algorithm is developed. Experiments of microarray classification are performed on the leukaemia data set to verify the obtained results.

关键词： Gene selection grouping effect microarray classification solution path support vector machine (SVM)

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