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Results for “bootstrap aggregation”

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At least 19 recordsLinked to original sources

PubMed1999

Inferential estimation of polymer quality using bootstrap aggregated neural networks.

Inferential estimation of polymer quality in a batch polymerisation reactor using bootstrap aggregated neural networks is studied in this paper. Number average molecular weight and weight average molecular weight are estimated from the on-line measurements of reactor temperature, jacket inlet and outlet temperatures, coolant flow rate through the jacket, monomer conversion, and the initial batch conditions. Bootstrap aggregated neural networks are used to enhance the accuracy and robustness of neural network models built from a limited amount of training data. The training data set is re-sampled using bootstrap re-sampling with replacement to form several sets of training data. For each set of training data, a neural network model is developed. The individual neural networks are then combined together to form a bootstrap aggregated neural network. Determination of appropriate weights for combining individual networks using principal component regression is proposed in this paper. Confidence bounds for neural network predictions can also be obtained using the bootstrapping technique. The techniques have been successfully applied to the simulation of a batch methyl methacrylate polymerisation reactor.

Journal Article↗

PubMed2002

Geographical information systems and bootstrap aggregation (bagging) of tree-based classifiers for Lyme disease risk prediction in Trentino, Italian Alps.

The risk of exposure to Lyme disease in the province of Trento, Italian Alps, was predicted through the analysis of the distribution of Ixodes ricinus (L.) nymphs infected with Borrelia burgdorferi s.l. with a model based on bootstrap aggregation (bagging) of tree-based classifiers within a geographical information system (GIS). Data on L ricinus density assessed by dragging the vegetation in 438 sites during 1996 were cross-correlated with the digital cartography of a GIS, which included the variables altitude, exposure and slope, substratum, vegetation type and roe deer density. Ticks were more abundant at altitudes below 1,300 m a.s.l., in the presence of limestone and vegetation cover with thermophile deciduous forests and high densities of roe deer. A bootstrap aggregation procedure (bagging) was used to produce a model for the prediction of tick occurrence, the accuracy of which was tested on actual tick counts assessed by a further dragging campaign carried out during 1997 to determine infection prevalence and resulted in average 77%. Other tests of the model were made on additional and independent data sets. The prevalence of infection with Borrelia burgdorferi s.l, determined by polymerase chain reaction on 2,208 nymphs collected by random dragging in 245 transects selected within eight areas where the model predicted the occurrence of I. ricinus during 1997, was 17.5% and was positively correlated to tick abundance and roe deer density. These findings were used to relate the output of the bagged model (probability of tick occurrence) to the density of infected nymphs through a stepwise model selection procedure and thus to produce a GIS digital map of the probability distribution of infected nymphs in the Province of Trento at high resolution scale (50 by 50-m cell resolution). The application of the bagging procedure increased the accuracy of the prediction made by a single classification tree, a well-known classification method for the analysis of epidemiological data.

Animals↗

PubMed2006

Using Bagging classifier to predict protein domain structural class.

Classification and prediction of protein domain structural class is one of the important topics in the molecular biology. We introduce the Bagging (Bootstrap aggregating), one of the bootstrap methods, for classifying and predicting protein structural classes. By a bootstrap aggregating procedure, the Bagging can improve a weak classifier, for instance the random tree method, to a significant step towards optimality. In this research, it is demonstrated that the Bagging performed at least as well as LogitBoost and Support vector machines in predicting the structural classes for a given protein domain dataset by 10 cross-validation test, which indicate that the Bagging method is promising and anticipated that it could be potentially further improved on predicting protein structural classes as well as other bio-macromolecular attributes, if the bagging method and other existing methods can be effectively complemented with each other.

Algorithms↗

PubMed2003

Diagnosis of glaucoma by indirect classifiers.

OBJECTIVES: Demonstration of the applicability of a framework called indirect classification to the example of glaucoma classification. Indirect classification combines medical a priori knowledge and statistical classification methods. The method is compared to direct classification approaches with respect to the estimated misclassification error. METHODS: Indirect classification is applied using classification trees and the diagnosis of glaucoma. Misclassification errors are reduced by bootstrap aggregation. As direct classification methods linear discriminant analysis, classification trees and bootstrap aggregated classification trees are utilized in the problem of glaucoma diagnosis. Misclassification rates are estimated via 10-fold cross-validation. RESULTS: Indirect classification techniques reduce the misclassification error in the context of glaucoma classification compared to direct classification methods. CONCLUSIONS: Embedding a priori knowledge into statistical classification techniques can improve misclassification results. Indirect classification offers a framework to realize this combination.

Decision Support Techniques↗

PubMed2005

Support vector machines committee classification method for computer-aided polyp detection in CT colonography.

RATIONALE AND OBJECTIVES: A new classification scheme for the computer-aided detection of colonic polyps in computed tomographic colonography is proposed. MATERIALS AND METHODS: The scheme involves an ensemble of support vector machines (SVMs) for classification, a smoothed leave-one-out (SLOO) cross-validation method for obtaining error estimates, and use of a bootstrap aggregation method for training and model selection. Our use of an ensemble of SVM classifiers with bagging (bootstrap aggregation), built on different feature subsets, is intended to improve classification performance compared with single SVMs and reduce the number of false-positive detections. The bootstrap-based model-selection technique is used for tuning SVM parameters. In our first experiment, two independent data sets were used: the first, for feature and model selection, and the second, for testing to evaluate the generalizability of our model. In the second experiment, the test set that contained higher resolution data was used for training and testing (using the SLOO method) to compare SVM committee and single SVM performance. RESULTS: The overall sensitivity on independent test set was 75%, with 1.5 false-positive detections/study, compared with 76%-78% sensitivity and 4.5 false-positive detections/study estimated using the SLOO method on the training set. The sensitivity of the SVM ensemble retrained on the former test set estimated using the SLOO method was 81%, which is 7%-10% greater than the sensitivity of a single SVM. The number of false-positive detections per study was 2.6, a 1.5 times reduction compared with a single SVM. CONCLUSION: Training an SVM ensemble on one data set and testing it on the independent data has shown that the SVM committee classification method has good generalizability and achieves high sensitivity and a low false-positive rate. The model selection and improved error estimation method are effective for computer-aided polyp detection.

Algorithms↗

PubMed2025

Learning directed acyclic graphs for ligands and receptors based on spatially resolved transcriptomic data of ovarian cancer.

To unravel the mechanism of immune activation and suppression within tumors, a critical step is to identify transcriptional signals governing cell-cell communication between tumor and immune/stromal cells in the tumor microenvironment. Central to this communication are interactions between secreted ligands and cell-surface receptors, creating a highly connected signaling network among cells. Recent advancements in in situ-omics profiling, particularly spatial transcriptomic (ST) technology, provide unique opportunities to directly characterize ligand-receptor signaling networks that power cell-cell communication. In this paper, we propose a novel statistical method, LRnetST, to characterize the ligand-receptor interaction networks between adjacent tumor and immune/stroma cells based on ST data. LRnetST utilizes a directed acyclic graph model with a novel approach to handle the zero-inflated distributions of ST data. It also leverages existing ligand-receptor regulation databases as prior information, and employs a bootstrap aggregation strategy to achieve robust network estimation. Application of LRnetST to ST data of high-grade serous ovarian tumor samples revealed both common and distinct ligand-receptor regulations across different tumors. Some of these interactions were validated through both a MERFISH dataset and a CosMx SMI dataset of independent ovarian tumor samples. These results cast light on biological processes relating to the communication between tumor and immune/stromal cells in ovarian tumors. An open-source R package of LRnetST is available on GitHub at https://github.com/jie108/LRnetST.

Humans↗

PubMed2000

Neural network schemes for detecting rare events in human genomic DNA.

MOTIVATION: Many problems in molecular biology as well as other areas involve detection of rare events in unbalanced data. We develop two sample stratification schemes in conjunction with neural networks for rare event detection in such databases. Sample stratification is a technique for making each class in a sample have equal influence on decision making. The first scheme proposed stratifies a sample by adding up the weighted sum of the derivatives during the backward pass of training. The second scheme proposed uses a technique of modified bootstrap aggregating. After training neural networks with multiple sets of bootstrapped examples of the rare event classes and subsampled examples of common event classes, multiple voting for classification is performed. RESULTS: These two schemes make rare event classes have a better chance of being included in the sample used for training neural networks and thus improve the classification accuracy for rare event detection. The experimental performance of the two schemes using two sets of human DNA sequences as well as another set of Gaussian data indicates that proposed schemes have the potential of significantly improving accuracy of neural networks to recognize rare events.

Algorithms↗

PubMed2006

Characterization of Escherichia coli isolates from different fecal sources by means of classification tree analysis of fatty acid methyl ester (FAME) profiles.

Microbial source tracking (MST) methods need to be rapid, inexpensive and accurate. Unfortunately, many MST methods provide a wealth of information that is difficult to interpret by the regulators who use this information to make decisions. This paper describes the use of classification tree analysis to interpret the results of a MST method based on fatty acid methyl ester (FAME) profiles of Escherichia coli isolates, and to present results in a format readily interpretable by water quality managers. Raw sewage E. coli isolates and animal E. coli isolates from cow, dog, gull, and horse were isolated and their FAME profiles collected. Correct classification rates determined with leaveone-out cross-validation resulted in an overall low correct classification rate of 61%. A higher overall correct classification rate of 85% was obtained when the animal isolates were pooled together and compared to the raw sewage isolates. Bootstrap aggregation or adaptive resampling and combining of the FAME profile data increased correct classification rates substantially. Other MST methods may be better suited to differentiate between different fecal sources but classification tree analysis has enabled us to distinguish raw sewage from animal E. coli isolates, which previously had not been possible with other multivariate methods such as principal component analysis and cluster analysis.

Animals↗

PubMed2005

Automated segmentation of the optic nerve head for diagnosis of glaucoma.

Glaucoma is the second most common cause of blindness worldwide. Low awareness and high costs connected to glaucoma are reasons to improve methods of screening and therapy. A well-established method for diagnosis of glaucoma is the examination of the optic nerve head using scanning-laser-tomography. This system acquires and analyzes the surface topography of the optic nerve head. The analysis that leads to a diagnosis of the disease depends on prior manual outlining of the optic nerve head by an experienced ophthalmologist. Our contribution presents a method for optic nerve head segmentation and its validation. The method is based on morphological operations, Hough transform, and an anchored active contour model. The results were validated by comparing the performance of different classifiers on data from a case-control study with contours of the optic nerve head manually outlined by an experienced ophthalmologist. We achieved the following results with respect to glaucoma diagnosis: linear discriminant analysis with 27.7% estimated error rate for automated segmentation (aut) and 26.8% estimated error rate for manual segmentation (man), classification trees with 25.2% (aut) and 22.0% (man) and bootstrap aggregation with 22.2% (aut) and 13.4% (man). It could thus be shown that our approach is suitable for automated diagnosis and screening of glaucoma.

Algorithms↗

PubMedDate not supplied

On the use of neural network ensembles in QSAR and QSPR.

Despite their growing popularity among neural network practitioners, ensemble methods have not been widely adopted in structure-activity and structure-property correlation. Neural networks are inherently unstable, in that small changes in the training set and/or training parameters can lead to large changes in their generalization performance. Recent research has shown that by capitalizing on the diversity of the individual models, ensemble techniques can minimize uncertainty and produce more stable and accurate predictors. In this work, we present a critical assessment of the most common ensemble technique known as bootstrap aggregation, or bagging, as applied to QSAR and QSPR. Although aggregation does offer definitive advantages, we demonstrate that bagging may not be the best possible choice and that simpler techniques such as retraining with the full sample can often produce superior results. These findings are rationalized using Krogh and Vedelsby's decomposition of the generalization error into a term that measures the average generalization performance of the individual networks and a term that measures the diversity among them. For networks that are designed to resist over-fitting, the benefits of aggregation are clear but not overwhelming.

Computer Simulation↗

PubMedDate not supplied

Prediction of protein retention times in anion-exchange chromatography systems using support vector regression.

Quantitative Structure-Retention Relationship (QSRR) models are developed for the prediction of protein retention times in anion-exchange chromatography systems. Topological, subdivided surface area, and TAE (Transferable Atom Equivalent) electron-density-based descriptors are computed directly for a set of proteins using molecular connectivity patterns and crystal structure geometries. A novel algorithm based on Support Vector Machine (SVM) regression has been employed to obtain predictive QSRR models using a two-step computational strategy. In the first step, a sparse linear SVM was utilized as a feature selection procedure to remove irrelevant or redundant information. Subsequently, the selected features were used to produce an ensemble of nonlinear SVM regression models that were combined using bootstrap aggregation (bagging) techniques, where various combinations of training and validation data sets were selected from the pool of available data. A visualization scheme (star plots) was used to display the relative importance of each selected descriptor in the final set of "bagged" models. Once these predictive models have been validated, they can be used as an automated prediction tool for virtual high-throughput screening (VHTS).

Anion Exchange Resins↗

PubMed2000

N-tuple network, CART and bagging.

Similarities between bootstrap aggregation (bagging) and N-tuple sampling are explored to propose a retina-free data-driven version of the N-tuple network, whose close analogies to aggregated regression trees, such as classification and regression trees (CART), lead to further architectural enhancements. Performance of the proposed algorithms is compared with the traditional versions of the N-tuple and CART networks on a number of regression problems. The architecture significantly outperforms conventional N-tuple networks while leading to more compact solutions and avoiding certain implementational pitfalls of the latter.

Algorithms↗

PubMed2005

Improving model robustness with bootstrapping -- application to optimal discriminant analysis for ordinal responses (ODAO).

OBJECTIVE: Recent results published by Coste et al. in discriminant analysis with ordinal responses showed the superiority of optimal discriminating analysis for ordinal responses (ODAO) both in terms of classification and simplicity of implementation compared to classic methods (Fisher's discrimination, logistic regression) applied to medical data (prognostics of burns) and to simulated data. Nevertheless, the solutions obtained by ODAO may be sensitive to re-sampling (i.e the estimated coefficients by ODAO may show excessive sensitivity to the training sample). This study proposes some solutions to control the fluctuations of sampling and to ensure model stability. METHODS: We used intensive computational methods and bootstrapping, at the outset of model building in order to reduce the sampling variability of estimated coefficients. Thus, the estimation of the coefficients was not based on the minimization of a classification criterion of the training sample, but on the minimization of an aggregate criterion of bootstrapped replications of a classification criterion. Five aggregate criteria were studied. RESULTS: The improvement in terms of robustness appeared in 30% of the test cases with moderate training sample size and 55% of those with small training sample size. CONCLUSION: Simulated test cases showed that bootstrapping can help construct more robust models in difficult classification situations and small training samples which are particularly frequent.

Burns↗

PubMed2000

Resolution of phylogenetic relationships among recently evolved species as a function of amount of DNA sequence: an empirical study based on woodpeckers (Aves: Picidae).

Synonymous substitutions in the 13 mitochondrial encoded protein genes form a large pool of characters that should approach the ideal for phylogenetic analysis of being independently and identically distributed. Pooling sequences from multiple mitochondrial protein-coding genes should result in statistically more powerful estimates of relationships among species that diverged sufficiently recently that most nucleotide substitutions are synonymous. Cytochrome oxidase I (COI) was sequenced for woodpecker species for which cytochrome b (cyt b) sequences were available. A pairing-design test based on the normal distribution indicated that cyt b evolves more rapidly than COI when all nucleotides are compared but their rates are equal for synonymous substitutions. Nearly all of the phylogenetically informative substitutions among woodpeckers are synonymous. Statistical support for relationships, as measured by bootstrap proportions, increased as the number of nucleotides increased from 1047 (cyt b) to 1512 (COI) to 2559 nucleotides (aggregate data set). Pseudo-bootstrap replicates showed the same trend and increasing the amount of sequence beyond the actual length of 2559 nucleotides to 5120 (2x) resulted in stronger bootstrap support, even though the amount of phylogenetic information was the same. However, the amount of sequence required to resolve an internode depends on the length of the internode and its depth in the phylogeny.

Animals↗

PubMed2004

Bagging survival trees.

Predicted survival probability functions of censored event free survival are improved by bagging survival trees. We suggest a new method to aggregate survival trees in order to obtain better predictions for breast cancer and lymphoma patients. A set of survival trees based on B bootstrap samples is computed. We define the aggregated Kaplan-Meier curve of a new observation by the Kaplan-Meier curve of all observations identified by the B leaves containing the new observation. The integrated Brier score is used for the evaluation of predictive models. We analyse data of a large trial on node positive breast cancer patients conducted by the German Breast Cancer Study Group and a smaller 'pilot' study on diffuse large B-cell lymphoma, where prognostic factors are derived from microarray expression values. In addition, simulation experiments underline the predictive power of our proposal.

Breast Neoplasms↗

PubMed2005

Efficiently measuring recognition performance with sparse data.

We examine methods for measuring performance in signal-detection-like tasks when each participant provides only a few observations. Monte Carlo simulations demonstrate that standard statistical techniques applied to a d' analysis can lead to large numbers of Type I errors (incorrectly rejecting a hypothesis of no difference). Various statistical methods were compared in terms of their Type I and Type II error (incorrectly accepting a hypothesis of no difference) rates. Our conclusions are the same whether these two types of errors are weighted equally or Type I errors are weighted more heavily. The most promising method is to combine an aggregate d' measure with a percentile bootstrap confidence interval, a computer-intensive nonparametric method of statistical inference. Researchers who prefer statistical techniques more commonly used in psychology, such as a repeated measures t test, should use gamma (Goodman & Kruskal, 1954), since it performs slightly better than or nearly as well as d'. In general, when repeated measures t tests are used, gamma is more conservative than d': It makes more Type II errors, but its Type I error rate tends to be much closer to that of the traditional .05 alpha level. It is somewhat surprising that gamma performs as well as it does, given that the simulations that generated the hypothetical data conformed completely to the d' model. Analyses in which H--FA was used had the highest Type I error rates. Detailed simulation results can be downloaded from www.psychonomic.org/archive/Schooler-BRM-2004.zip.

Computer Simulation↗

PubMed2006

Evaluating the performance of species richness estimators: sensitivity to sample grain size.

1. Fifteen species richness estimators (three asymptotic based on species accumulation curves, 11 nonparametric, and one based in the species-area relationship) were compared by examining their performance in estimating the total species richness of epigean arthropods in the Azorean Laurisilva forests. Data obtained with standardized sampling of 78 transects in natural forest remnants of five islands were aggregated in seven different grains (i.e. ways of defining a single sample): islands, natural areas, transects, pairs of traps, traps, database records and individuals to assess the effect of using different sampling units on species richness estimations. 2. Estimated species richness scores depended both on the estimator considered and on the grain size used to aggregate data. However, several estimators (ACE, Chao 1, Jackknifel and 2 and Bootstrap) were precise in spite of grain variations. Weibull and several recent estimators [proposed by Rosenzweig et al. (Conservation Biology, 2003, 17, 864-874), and Ugland et al. (Journal of Animal Ecology, 2003, 72, 888-897)] performed poorly. 3. Estimations developed using the smaller grain sizes (pair of traps, traps, records and individuals) presented similar scores in a number of estimators (the above-mentioned plus ICE, Chao2, Michaelis-Menten, Negative Exponential and Clench). The estimations from those four sample sizes were also highly correlated. 4. Contrary to other studies, we conclude that most species richness estimators may be useful in biodiversity studies. Owing to their inherent formulas, several nonparametric and asymptotic estimators present insensitivity to differences in the way the samples are aggregated. Thus, they could be used to compare species richness scores obtained from different sampling strategies. Our results also point out that species richness estimations coming from small grain sizes can be directly compared and other estimators could give more precise results in those cases. We propose a decision framework based on our results and on the literature to assess which estimator should be used to compare species richness scores of different sites, depending on the grain size of the original data, and of the kind of data available (species occurrence or abundance data).

Animals↗

PubMed1998

Comparative performance of species richness estimation methods.

In most real-world contexts the sampling effort needed to attain an accurate estimate of total species richness is excessive. Therefore, methods to estimate total species richness from incomplete collections need to be developed and tested. Using real and computer-simulated parasite data sets, the performances of 9 species richness estimation methods were compared. For all data sets, each estimation method was used to calculate the projected species richness at increasing levels of sampling effort. The performance of each method was evaluated by calculating the bias and precision of its estimates against the known total species richness. Performance was evaluated with increasing sampling effort and across different model communities. For the real data sets, the Chao2 and first-order jackknife estimators performed best. For the simulated data sets, the first-order jackknife estimator performed best at low sampling effort but, with increasing sampling effort, the bootstrap estimator outperformed all other estimators. Estimator performance increased with increasing species richness, aggregation level of individuals among samples and overall population size. Overall, the Chao2 and the first-order jackknife estimation methods performed best and should be used to control for the confounding effects of sampling effort in studies of parasite species richness. Potential uses of and practical problems with species richness estimation methods are discussed.

Animals↗