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Algorithms for index-assisted selectivity estimation

P.M. Aoki
1999 Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337)  
We discuss the computation of selectivity estimates with confidence intervals over arbitrary data types using indices, give methods for combining this technique with random sampling, and present results  ...  from an experimental comparison of these methods with several estimators from the literature.  ...  Acknowledgements The Blobworld data was obtained from Chad Carson and Megan Thomas.  ... 
doi:10.1109/icde.1999.754938 dblp:conf/icde/Aoki99 fatcat:66kivnf5gndzhl753m7maham4q

Distance metric learning for RRT-based motion planning with constant-time inference

Luigi Palmieri, Kai O. Arras
2015 2015 IEEE International Conference on Robotics and Automation (ICRA)  
two-point boundary value problem for wheeled mobile robots, we train a simple nonlinear parametric model with constant-time inference that is shown to predict distances accurately in terms of regression and ranking  ...  A key component in the extension of the tree in RRT is the distance pseudo-metric, or cost-to-go pseudo-metric, used to select the nearest vertex from where to grow the tree.  ...  Rapidly exploring Random Trees (RRT) solve a single planning query by growing and expanding a tree in the configuration space towards newly sampled configurations.  ... 
doi:10.1109/icra.2015.7139246 dblp:conf/icra/PalmieriA15 fatcat:zq7yj4uiebfc3ceillgxplfjbq

A Conformal Prediction Approach to Explore Functional Data [article]

Jing Lei, Alessandro Rinaldo, Larry Wasserman
2013 arXiv   pre-print
Both our prediction bands and clustering trees provide prediction sets for the underlying stochastic process with a guaranteed finite sample behavior, under no distributional assumptions.  ...  This paper applies conformal prediction techniques to compute simultaneous prediction bands and clustering trees for functional data. These tools can be used to detect outliers and clusters.  ...  As the conformal parameter α varies from 0 to 1, the collection T of all level α clusters form a tree (i.e. A, B ∈ T implies that A ∩ B = ∅ or A ⊂ B or B ⊂ A), which we call the conformal tree.  ... 
arXiv:1302.6452v1 fatcat:ui6ea4nrebbgloznbb7rquarn4

Identification of Penicillin-binding proteins employing support vector machines and random forest

Vinay Nair, Monalisa Dutta, Sowmya S Manian, Ramya Kumari S, Valadi K Jayaraman
2013 Bioinformation  
We then created models to classify the proteins into Penicillin-Binding and non-binding using supervised machine learning algorithms such as Support Vector Machines and Random Forest.  ...  Acknowledgement: V.K.J gratefully acknowledges financial support from Department of Science and Technology, New Delhi.  ...  Model 1 consisted of 744 samples of PBP and non-PBP proteins. Model 2 consisted of 100 samples each of Class A, Class B and LMM PBP proteins.  ... 
doi:10.6026/97320630009481 pmid:23847404 pmcid:PMC3705620 fatcat:l3asb5sgqncg5dm2ckxkt5enpe

Modeling the distribution of a wide‐ranging invasive species using the sampling efforts of expert and citizen scientists

Emilie Roy‐Dufresne, Frédérik Saltré, Brian D. Cooke, Camille Mellin, Greg Mutze, Tarnya Cox, Damien A. Fordham
2019 Ecology and Evolution  
The additional sampling effort provided by citizens can improve the capacity of SDMs to capture important elements of a species ecological niche, improving the capacity of statistical models to accurately  ...  advantage of incorporating data collected by citizens (separately and jointly with expert data) and explored issues of spatial biases in occurrence data by implementing different approaches to generate pseudo-absences  ...  Since both the test and training datasets are sampled from the same initial set of data, they are similarly biased, resulting in evaluation scores indistinguishable from models with random pseudo-absences  ... 
doi:10.1002/ece3.5609 pmid:31641454 pmcid:PMC6802020 fatcat:52ta53g5pfgmjli3sjmbeywwvu

Do pseudo-absence selection strategies influence species distribution models and their predictions? An information-theoretic approach based on simulated data

Mary S Wisz, Antoine Guisan
2009 BMC Ecology  
We evaluated the effect of using a) real absences b) pseudo-absences selected randomly from the background and c) two-step approaches: pseudo-absences selected from low suitability areas predicted by either  ...  Models based on random pseudo-absences had among the lowest fit, but yielded the second highest AUC value (0.97), and the "true" model was also supported by the data.  ...  Define Define virtual species' distribution from model including tree cover, treecover 2 pseudo-absence threshold based on pseudo-absences.  ... 
doi:10.1186/1472-6785-9-8 pmid:19393082 pmcid:PMC2680809 fatcat:mqtubw74dnctjmx4aefcbsu3fe

Modelling and mapping heavy metal and nitrogen concentrations in moss in 2010 throughout Europe by applying Random Forests models

Stefan Nickel, Winfried Schröder, Werner Wosniok, Harry Harmens, Marina V. Frontasyeva, Renate Alber, Julia Aleksiayenak, Lambe Barandovski, Oleg Blum, Helena Danielsson, Ludwig de Temmermann, Anatoly M. Dunaev (+28 others)
2017 Atmospheric Environment  
Forests (RF) and Classification and Regression Trees (CART).  ...  RF is an eligible method identifying and ranking boundary conditions of element 50 concentrations in moss and related mapping including the influence of the environmental factors.  ...  Each tree is constructed using a bootstrap sample from the original data set, and the 180 predictions of all trees are finally aggregated.  ... 
doi:10.1016/j.atmosenv.2017.02.032 fatcat:zhmp5xab7fbbleqxdbd46vb2ma

MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features

P. Jiang, H. Wu, W. Wang, W. Ma, X. Sun, Z. Lu
2007 Nucleic Acids Research  
To distinguish the real pre-miRNAs from other hairpin sequences with similar stem-loops (pseudo pre-miRNAs), a hybrid feature which consists of local contiguous structure-sequence composition, minimum  ...  of free energy (MFE) of the secondary structure and P-value of randomization test is used.  ...  Estimating and ranking the feature importance Decision tree is known for its ability to select 'important' ones from many features and ignore (often irrelevant) others.  ... 
doi:10.1093/nar/gkm368 pmid:17553836 pmcid:PMC1933124 fatcat:mqwjydm6u5hjtmbrv5k4idebeu

Developing Sustainable Classification of Diseases via Deep Learning and Semi-Supervised Learning

Chunwu Yin, Zhanbo Chen
2020 Healthcare  
to update a mechanism designed to increase the number of high-confidence pseudo-labelled samples.  ...  Self-training can be implemented using high- to low-confidence samples to prevent noisy samples from affecting the robustness of semi-supervised learning in the training process.  ...  Furthermore, the deep forest exploits two types of forests, i.e., random forests (RFs) and completely random tree forests, which help enhance the diversity.  ... 
doi:10.3390/healthcare8030291 pmid:32846941 fatcat:5bz4hwc2gbfw7jnrdjoli33jai

An effective tumor classification with deep forest and self-training

Zhanbo Chen, Zhanbo Chen, Xiaojun Sun, Lili Shen
2021 IEEE Access  
the cost; that is, a updated unlabelled sample mechanism is investigated to expand the number of high-confidence pseudo-labelled samples.  ...  We wish training style that samples can be implemented to train by from high-to low-confidence, self-training can meet this requirement, and the deep forest approach with the hyper-parameter settings used  ...  initial pseudo-labelled samples' weights v 0 Output: Model output result v, w Learn a classifier f by deep forest from labelled data to obtain label y from unlabelled data Obtain L based v: solve Equation  ... 
doi:10.1109/access.2021.3096241 fatcat:imot75ahnjglxmef6mbua37s6i

Biodiversity Mapping in a Tropical West African Forest with Airborne Hyperspectral Data

Gaia Vaglio Laurin, Jonathan Cheung-Wai Chan, Qi Chen, Jeremy A. Lindsell, David A. Coomes, Leila Guerriero, Fabio Del Frate, Franco Miglietta, Riccardo Valentini, Michael Sears
2014 PLoS ONE  
Shannon-Wiener indices were then predicted from these plot-based reflectance statistics using a machine-learning algorithm (Random Forest).  ...  The abundance of tree species were collected from 64 plots (each 1250 m 2 in size) within a Sierra Leonean national park, and Shannon-Wiener biodiversity indices were calculated.  ...  Field plot data were collected by staff of the Gola Forest Programme, with funding from the UK's Darwin Initiative. We are very grateful to Mr. Alosine Fofana, Guy Marris, Dr. Annika Hillers and Dr.  ... 
doi:10.1371/journal.pone.0097910 pmid:24937407 pmcid:PMC4060990 fatcat:23je2t7hzbgdhjfhpefzne5s4i

Face Alignment Using a Ranking Model based on Regression Trees

Hua Gao, Hazim Ekenel, Rainer Stiefelhagen
2012 Procedings of the British Machine Vision Conference 2012  
To avoid the overfitting problem in gradient boosting, we use random trees to initialize the boosting. The Nelder Mead's simplex method is applied for fitting the learned model.  ...  In this work, we exploit the regression trees-based ranking model, which has been successfully applied in the domain of web-search ranking, to build appearance models for face alignment.  ...  The second proposed ranking model is based on the gradient boosted regression trees. The random forests technique is used to initialize the GBRT training iter-ations.  ... 
doi:10.5244/c.26.118 dblp:conf/bmvc/GaoES12 fatcat:m5agk5mvmzb2nlq4fdtsdsjkzy

Faster Subset Selection for Matrices and Applications

Haim Avron, Christos Boutsidis
2013 SIAM Journal on Matrix Analysis and Applications  
We study the following problem of subset selection for matrices: given a matrix X ∈ R n×m (m > n) and a sampling parameter k (n ≤ k ≤ m), select a subset of k columns from X such that the pseudo-inverse  ...  of the sampled matrix has as smallest norm as possible.  ...  The authors acknowledge the support from XDATA program of the Defense Advanced Research Projects Agency (DARPA), administered through Air Force Research Laboratory contract FA8750-12-C-0323.  ... 
doi:10.1137/120867287 fatcat:25gxox57krhjfghppxvwodc3wy

Gene-Gene Interaction AmongWNTGenes for Oral Cleft in Trios

Qing Li, Yoonhee Kim, Bhoom Suktitipat, Jacqueline B. Hetmanski, Mary L. Marazita, Priya Duggal, Terri H. Beaty, Joan E. Bailey-Wilson
2015 Genetic Epidemiology  
Our group benefited greatly from the work of the Coordinating Center (directed by B. Weir and C.  ...  Funding to support data collection, genotyping and analysis came from several sources, some to individual investigators and some to the consortium itself.  ...  Here, decision trees are trained using bootstrap samples of CL/P cases and pseudo-controls (selected with replacement) and random subsets of predictor variables, and tested on out-of-bag (OOB) samples  ... 
doi:10.1002/gepi.21888 pmid:25663376 pmcid:PMC4469492 fatcat:zfqezjgqefasrdkd4spfwlhgwa

Towards patterns tree of gene coexpression in eukaryotic species

Haiyun Wang, Qi Wang, Xia Li, Bairong Shen, Min Ding, Ziyin Shen
2008 Computer applications in the biosciences : CABIOS  
Many genes from the different pathway also present coexpression patterns.  ...  The patterns trees of different species give us comprehensive insight and understanding of genes expression activity in the cellular society. Contact:  ...  Middle panel in A, B and C is the distribution of Spearman rank correlation. Right panel in A, B and C is the distribution of mutual information. Fig. 3 . 3 Gene coexpression patterns tree.  ... 
doi:10.1093/bioinformatics/btn134 pmid:18407921 fatcat:ogzr3e4np5hbrecswrjt5xxtky
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