Journal articles: 'Leave one out method'

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Krzanowski, W. J., and D. J. Hand. "ASSESSING ERROR RATE ESTIMATORS: THE LEAVE-ONE-OUT METHOD RECONSIDERED." Australian Journal of Statistics 39, no. 1 (March 1997): 35–46. http://dx.doi.org/10.1111/j.1467-842x.1997.tb00521.x.

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Zhang, Tong. "Leave-One-Out Bounds for Kernel Methods." Neural Computation 15, no. 6 (June 1, 2003): 1397–437. http://dx.doi.org/10.1162/089976603321780326.

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In this article, we study leave-one-out style cross-validation bounds for kernel methods. The essential element in our analysis is a bound on the parameter estimation stability for regularized kernel formulations. Using this result, we derive bounds on expected leave-one-out cross-validation errors, which lead to expected generalization bounds for various kernel algorithms. In addition, we also obtain variance bounds for leave-oneout errors. We apply our analysis to some classification and regression problems and compare them with previous results.

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Li, Xiping, David Tripe, Chris Malone, and David Smith. "Measuring systemic risk contribution: The leave-one-out z-score method." Finance Research Letters 36 (October 2020): 101316. http://dx.doi.org/10.1016/j.frl.2019.101316.

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Brovelli, Maria Antonia, Mattia Crespi, Francesca Fratarcangeli, Francesca Giannone, and Eugenio Realini. "Accuracy assessment of high resolution satellite imagery orientation by leave-one-out method." ISPRS Journal of Photogrammetry and Remote Sensing 63, no. 4 (July 2008): 427–40. http://dx.doi.org/10.1016/j.isprsjprs.2008.01.006.

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ALPTEKIN, AHMET, and OLCAY KURSUN. "MISS ONE OUT: A CROSS-VALIDATION METHOD UTILIZING INDUCED TEACHER NOISE." International Journal of Pattern Recognition and Artificial Intelligence 27, no. 07 (November 2013): 1351003. http://dx.doi.org/10.1142/s0218001413510038.

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Leave-one-out (LOO) and its generalization, K-Fold, are among most well-known cross-validation methods, which divide the sample into many folds, each one of which is, in turn, left out for testing, while the other parts are used for training. In this study, as an extension of this idea, we propose a new cross-validation approach that we called miss-one-out (MOO) that mislabels the example(s) in each fold and keeps this fold in the training set as well, rather than leaving it out as LOO does. Then, MOO tests whether the trained classifier can correct the erroneous label of the training sample. In principle, having only one fold deliberately labeled incorrectly should have only a small effect on the classifier that uses this bad-fold along with K - 1 good folds and can be utilized as a generalization measure of the classifier. Experimental results on a number of benchmark datasets and three real bioinformatics dataset show that MOO can better estimate the test set accuracy of the classifier.

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Montoya Perez, Ileana, Antti Airola, Peter J. Boström, Ivan Jambor, and Tapio Pahikkala. "Tournament leave-pair-out cross-validation for receiver operating characteristic analysis." Statistical Methods in Medical Research 28, no. 10-11 (August 20, 2018): 2975–91. http://dx.doi.org/10.1177/0962280218795190.

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Receiver operating characteristic analysis is widely used for evaluating diagnostic systems. Recent studies have shown that estimating an area under receiver operating characteristic curve with standard cross-validation methods suffers from a large bias. The leave-pair-out cross-validation has been shown to correct this bias. However, while leave-pair-out produces an almost unbiased estimate of area under receiver operating characteristic curve, it does not provide a ranking of the data needed for plotting and analyzing the receiver operating characteristic curve. In this study, we propose a new method called tournament leave-pair-out cross-validation. This method extends leave-pair-out by creating a tournament from pair comparisons to produce a ranking for the data. Tournament leave-pair-out preserves the advantage of leave-pair-out for estimating area under receiver operating characteristic curve, while it also allows performing receiver operating characteristic analyses. We have shown using both synthetic and real-world data that tournament leave-pair-out is as reliable as leave-pair-out for area under receiver operating characteristic curve estimation and confirmed the bias in leave-one-out cross-validation on low-dimensional data. As a case study on receiver operating characteristic analysis, we also evaluate how reliably sensitivity and specificity can be estimated from tournament leave-pair-out receiver operating characteristic curves.

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Belotti, Federico, and Franco Peracchi. "Fast leave-one-out methods for inference, model selection, and diagnostic checking." Stata Journal: Promoting communications on statistics and Stata 20, no. 4 (December 2020): 785–804. http://dx.doi.org/10.1177/1536867x20976312.

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In this article, we describe jackknife2, a new prefix command for jackknifing linear estimators. It takes full advantage of the available leave-one-out formula, thereby allowing for substantial reduction in computing time. Of special note is that jackknife2 allows the user to compute cross-validation and diagnostic measures that are currently not available after ivregress 2sls, xtreg, and xtivregress.

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Lee, M. M. S., S. S. Keerthi, C. J. Ong, and D. DeCoste. "An Efficient Method for Computing Leave-One-Out Error in Support Vector Machines With Gaussian Kernels." IEEE Transactions on Neural Networks 15, no. 3 (May 2004): 750–57. http://dx.doi.org/10.1109/tnn.2004.824266.

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Bo, Liefeng, Ling Wang, and Licheng Jiao. "Feature Scaling for Kernel Fisher Discriminant Analysis Using Leave-One-Out Cross Validation." Neural Computation 18, no. 4 (April 1, 2006): 961–78. http://dx.doi.org/10.1162/neco.2006.18.4.961.

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Kernel fisher discriminant analysis (KFD) is a successful approach to classification. It is well known that the key challenge in KFD lies in the selection of free parameters such as kernel parameters and regularization parameters. Here we focus on the feature-scaling kernel where each feature individually associates with a scaling factor. A novel algorithm, named FS-KFD, is developed to tune the scaling factors and regularization parameters for the feature-scaling kernel. The proposed algorithm is based on optimizing the smooth leave-one-out error via a gradient-descent method and has been demonstrated to be computationally feasible. FS-KFD is motivated by the following two fundamental facts: the leave-one-out error of KFD can be expressed in closed form and the step function can be approximated by a sigmoid function. Empirical comparisons on artificial and benchmark data sets suggest that FS-KFD improves KFD in terms of classification accuracy.

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Lv, Liye, Xueguan Song, and Wei Sun. "Modify Leave-One-Out Cross Validation by Moving Validation Samples around Random Normal Distributions: Move-One-Away Cross Validation." Applied Sciences 10, no. 7 (April 3, 2020): 2448. http://dx.doi.org/10.3390/app10072448.

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The leave-one-out cross validation (LOO-CV), which is a model-independent evaluate method, cannot always select the best of several models when the sample size is small. We modify the LOO-CV method by moving a validation point around random normal distributions—rather than leaving it out—naming it the move-one-away cross validation (MOA-CV), which is a model-dependent method. The key point of this method is to improve the accuracy rate of model selection that is unreliable in LOO-CV without enough samples. Errors from LOO-CV and MOA-CV, i.e., LOO-CVerror and MOA-CVerror, respectively, are employed to select the best one of four typical surrogate models through four standard mathematical functions and one engineering problem. The coefficient of determination (R-square, R2) is used to be a calibration of MOA-CVerror and LOO-CVerror. Results show that: (i) in terms of selecting the best models, MOA-CV and LOO-CV become better as sample size increases; (ii) MOA-CV has a better performance in selecting best models than LOO-CV; (iii) in the engineering problem, both the MOA-CV and LOO-CV can choose the worst models, and in most cases, MOA-CV has a higher probability to select the best model than LOO-CV.

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Madad, Ali, Mossayeb Jamshid, Ali Reza Gharagozlou, Ali Reza Vafaei Nejad, Ali Javidane, and Hamid Reza Ranjbar. "Spatial Analysis Approach in Revealing the Global Sinks of Atmosphere Carbon Dioxide through “Leave One Out” Method." Journal of Geographic Information System 06, no. 04 (2014): 286–97. http://dx.doi.org/10.4236/jgis.2014.64026.

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Kroopnick, Marc H., Jinsong Chen, Jaehwa Choi, and C. Mitchell Dayton. "Assessing Classification Bias in Latent Class Analysis: Comparing Resubstitution and Leave-One-Out Methods." Journal of Modern Applied Statistical Methods 9, no. 1 (May 1, 2010): 52–63. http://dx.doi.org/10.22237/jmasm/1272686760.

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Jiao, Long, Xiaofei Wang, LI Hua, and Yunxia Wang. "QSPR study on the gas/particle partition coefficient of PCBs by using molecular distance-edge vector index." Journal of the Serbian Chemical Society 79, no. 8 (2014): 965–75. http://dx.doi.org/10.2298/jsc130611152j.

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The quantitative structure property relationship (QSPR) for gas/particle partition coefficient, Kp, of polychlorinated biphenyls (PCBs) was investigated. Molecular distance-edge vector (MDEV) index was used as the structural descriptor of PCBs. The quantitative relationship between the MDEV index and log Kp was modeled by multivariate linear regression (MLR) and artificial neural network (ANN) respectively. Leave one out cross validation and external validation were carried out to assess the prediction ability of the developed models. When the MLR method is used, the root mean square relative error (RMSRE) of prediction for leave one out cross validation and external validation is 4.72 and 8.62 respectively. When the ANN method is employed, the prediction RMSRE of leave one out cross validation and external validation is 3.87 and 7.47 respectively. It is demonstrated that the developed models are practicable for predicting the Kp of PCBs. The MDEV index is shown to be quantitatively related to the Kp of PCBs.

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Cheng, Jian, Rohan Fernando, and Jack C. Dekkers. "32 Cross validation of best linear unbiased predictions of breeding values using an efficient leave-one-out strategy." Journal of Animal Science 98, Supplement_4 (November 3, 2020): 10–11. http://dx.doi.org/10.1093/jas/skaa278.020.

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Abstract Efficient strategies have been developed for leave-one-out cross validation (LOOCV) of predicted phenotypes in a simple model with an overall mean and marker effects or animal genetic effects to evaluate the accuracy of genomic predictions. For such a model, the correlation between the predicted and the observed phenotype is identical to the correlation between the observed phenotype and the estimated breeding value (EBV). When the model is more complex, with multiple fixed and random effects, although the correlation between the observed and predicted phenotype can be obtained efficiently by LOOCV, it is not equal to the correlation between the observed phenotype and EBV, which is the statistic of interest. The objective here was to develop and evaluate an efficient LOOCV method for EBV or for predictions of other random effects under a general mixed linear model. The approach is based on treated all effects in the model, with large variances for fixed effects. Naïve LOOCV requires inverting the (n - 1) x (n - 1) dimensional phenotypic covariance matrix for each of the n (= no. observations) training data sets. Our method efficiently obtains these inverses from the inverse of the phenotypic covariance matrix for all n observations. Naïve LOOCV of EBV by pre-correction of fixed effects using the training data (Naïve LOOCV) and the new efficient LOOCV were compared. The new efficient LOOCV for EBV was 962 times faster than Naïve LOOCV. Prediction accuracies from the two strategies were the same (0.20). Funded by USDA-NIFA grant # 2017-67007-26144.

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Nolte, Ilja M. "Metasubtract: an R-package to analytically produce leave-one-out meta-analysis GWAS summary statistics." Bioinformatics 36, no. 16 (July 21, 2020): 4521–22. http://dx.doi.org/10.1093/bioinformatics/btaa570.

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Abstract Summary Summary statistics from a meta-analysis of genome-wide association studies (meta-GWAS) can be used for many follow-up analyses. One valuable application is the creation of polygenic scores. However, if polygenic scores are calculated in a validation cohort that was part of the meta-GWAS consortium, this cohort is not independent and analyses will therefore yield inflated results. The R package ‘MetaSubtract’ was developed to subtract the results of the validation cohort from meta-GWAS summary statistics analytically. The statistical formulas for a meta-analysis were inverted to compute corrected summary statistics of a meta-GWAS leaving one (or more) cohort(s) out. These formulas have been implemented in MetaSubtract for different meta-analyses methods (fixed effects inverse variance or square root sample size weighted z-score) accounting for no, single or double genomic control correction. Results obtained by MetaSubtract correlate very well to those calculated using the traditional way, i.e. by performing a meta-analysis leaving out the validation cohort. In conclusion, MetaSubtract allows researchers to compute meta-GWAS summary statistics that are independent of the GWAS results of the validation cohort without requiring access to the cohort level GWAS results of the corresponding meta-GWAS consortium. Availability and implementation https://cran.r-project.org/web/packages/MetaSubtract. Supplementary information Supplementary data are available at Bioinformatics online.

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Jiao, Long, Xiaofei Wang, Shan Bing, Zhiwei Xue, and Hua Li. "QSPR study of supercooled liquid vapour pressures of PBDEs by using molecular distance-edge vector index." Journal of the Serbian Chemical Society 80, no. 4 (2015): 499–508. http://dx.doi.org/10.2298/jsc140716087j.

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The quantitative structure property relationship (QSPR) for supercooled liquid vapour pressures (PL) of PBDEs was investigated. Molecular distance-edge vector (MDEV) index was used as the structural descriptor. The quantitative relationship between the MDEV index and lgPL was modeled by using multivariate linear regression (MLR) and artificial neural network (ANN) respectively. Leave-one-out cross validation and k-fold cross validation were carried out to assess the prediction ability of the developed models. For the MLR method, the prediction root mean square relative error (RMSRE) of leave-one-out cross validation and k-fold cross validation is 9.95 and 9.05 respectively. For the ANN method, the prediction RMSRE of leave-one-out cross validation and k-fold cross validation is 8.75 and 8.31 respectively. It is demonstrated the established models are practicable for predicting the lgPL of PBDEs. The MDEV index is quantitatively related to the lgPL of PBDEs. MLR and L-ANN are practicable for modeling this relationship. Compared with MLR, ANN shows slightly higher prediction accuracy. Subsequently, an MLR model, which regression equation is lgPL = 0.2868 M11 - 0.8449 M12 - 0.0605, and an ANN model, which is a two inputs linear network, were developed. The two models can be used to predict the lgPL of each PBDE.

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Walczak, Steven, and Vic Velanovich. "Identification of Preoperative Clinical Factors Associated With Perioperative Blood Transfusions." International Journal of Health Systems and Translational Medicine 1, no. 1 (January 2021): 62–75. http://dx.doi.org/10.4018/ijhstm.2021010103.

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Predicting patients' surgical transfusion needs preoperatively enables more efficient blood resource management. Identifying the significance of variables to use for transfusion predictions may be accomplished more reliably using machine learning, specifically artificial neural networks (ANN). A logistic regression model and two ANN programs are used to identify the contribution of nine variables selected following a literature review. The first ANN uses a sum of the weights method to identify variable contribution and the second ANN uses a leave one out strategy to identify variable contribution. All models indicated that hematocrit was the most significant variable for predicting perioperative blood transfusions. The weighted averages method indicated wRVU's and ASA score were the next most significant contributors. The leave one out method identified sex and INR as contributing to transfusion prediction. The importance of the variables other than hematocrit varied between techniques and may be dependent on the modeling method used.

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Jiao, Long, Shan Bing, Xiaofeng Zhang, and Hua Li. "Interval partial least squares and moving window partial least squares in determining the enantiomeric composition of tryptophan by using UV-Vis spectroscopy." Journal of the Serbian Chemical Society 81, no. 2 (2016): 209–18. http://dx.doi.org/10.2298/jsc150227065j.

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The application of interval partial least squares (IPLS) and moving window partial least squares (MWPLS) to the enantiomeric analysis of tryptophan (Trp) was investigated. A UV-Vis spectroscopy method for determining the enantiomeric composition of Trp was developed. The calibration model was built by using partial least squares (PLS), IPLS and MWPLS respectively. Leave-one-out cross validation and external test validation were used to assess the prediction performance of the established models. The validation result demonstrates the established full-spectrum PLS model is impractical for quantifying the relationship between the spectral data and enantiomeric composition of L-Trp. On the contrary, the developed IPLS and MWPLS model are both practicable for modeling this relationship. For the IPLS model, the root mean square relative error (RMSRE) of external test validation and leave-one-out cross validation is 4.03 and 6.50 respectively. For the MWPLS model, the RMSRE of external test validation and leave-one-out cross validation is 2.93 and 4.73 respectively. Obviously, the prediction accuracy of the MWPLS model is higher than that of the IPLS model. It is demonstrated UV-Vis spectroscopy combined with MWPLS is a commendable method for determining the enantiomeric composition of Trp. MWPLS is superior to IPLS for selecting spectral region in UV-Vis spectroscopy analysis.

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Hssayeni, Murtadha D., Joohi Jimenez-Shahed, Michelle A. Burack, and Behnaz Ghoraani. "Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements." Sensors 19, no. 19 (September 28, 2019): 4215. http://dx.doi.org/10.3390/s19194215.

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Tremor is one of the main symptoms of Parkinson’s Disease (PD) that reduces the quality of life. Tremor is measured as part of the Unified Parkinson Disease Rating Scale (UPDRS) part III. However, the assessment is based on onsite physical examinations and does not fully represent the patients’ tremor experience in their day-to-day life. Our objective in this paper was to develop algorithms that, combined with wearable sensors, can estimate total Parkinsonian tremor as the patients performed a variety of free body movements. We developed two methods: an ensemble model based on gradient tree boosting and a deep learning model based on long short-term memory (LSTM) networks. The developed methods were assessed on gyroscope sensor data from 24 PD subjects. Our analysis demonstrated that the method based on gradient tree boosting provided a high correlation (r = 0.96 using held-out testing and r = 0.93 using subject-based, leave-one-out cross-validation) between the estimated and clinically assessed tremor subscores in comparison to the LSTM-based method with a moderate correlation (r = 0.84 using held-out testing and r = 0.77 using subject-based, leave-one-out cross-validation). These results indicate that our approach holds great promise in providing a full spectrum of the patients’ tremor from continuous monitoring of the subjects’ movement in their natural environment.

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Yu, Qi, Yoan Miche, Antti Sorjamaa, Alberto Guillen, Amaury Lendasse, and Eric Séverin. "OP-KNN: Method and Applications." Advances in Artificial Neural Systems 2010 (March 24, 2010): 1–6. http://dx.doi.org/10.1155/2010/597373.

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This paper presents a methodology named Optimally Pruned K-Nearest Neighbors (OP-KNNs) which has the advantage of competing with state-of-the-art methods while remaining fast. It builds a one hidden-layer feedforward neural network using K-Nearest Neighbors as kernels to perform regression. Multiresponse Sparse Regression (MRSR) is used in order to rank each kth nearest neighbor and finally Leave-One-Out estimation is used to select the optimal number of neighbors and to estimate the generalization performances. Since computational time of this method is small, this paper presents a strategy using OP-KNN to perform Variable Selection which is tested successfully on eight real-life data sets from different application fields. In summary, the most significant characteristic of this method is that it provides good performance and a comparatively simple model at extremely high-learning speed.

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ASADOLLAHI-BABOLI, M., and A. MANI-VARNOSFADERANI. "APPLICATION OF COMPUTATIONAL METHODS TO PREDICT ABSORPTION MAXIMA OF ORGANIC DYES USED IN SOLAR CELLS." Journal of Theoretical and Computational Chemistry 12, no. 02 (March 2013): 1250114. http://dx.doi.org/10.1142/s0219633612501143.

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A quantitative structure-property relationship (QSPR) study for the prediction of the absorption maxima (λmax) of organic dyes used in solar cells was carried out using different computational methods. Three-dimensional (3D) descriptors were calculated using Codessa and Dragon softwares to represent the dye molecules. Then, different chemometric tools such as multivariate adaptive regression splines (MARS) and adaptive neuro-fuzzy inference system (ANFIS) combined with Monte Carlo (MC) sampling technique were utilized for selecting the most important descriptors and predicting the absorption maxima of the dyes. Various evaluation techniques such as leave-one-out, leave-multiple-out cross-validation procedures, randomization tests, and validation through the external test set were performed to validate the performance of the model. The results revealed that the calculated absorption maxima values are in good agreement with the experimental ones. This theoretical method provides an accurate and alternative method to obtain λmax of dyes before they are actually synthesized.

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Fredicia, Fredicia, Agus Buono, and Endang Purnama Giri. "Pengembangan Model Pengenalan Wajah Manusia dengan Teknik Reduksi Dimensi Bi2DPCA dan Support Vector Machine sebagai Classifier." Jurnal ULTIMATICS 8, no. 1 (March 20, 2017): 11–15. http://dx.doi.org/10.31937/ti.v8i1.497.

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This paper presents the modeling of face recognition using feature extraction based on Principal Component Analysis (PCA) and Support Vector Machine (SVM) as a classifier. Three PCA techniques were compared, they are 1DPCA, 2DPCA and Bi-2DPCA. Meanwhile, three type of SVM kernel functions-linear, polynomial, and radial basis function (RBF) were used. The experiment used the ORL Face Database AT&T Laboratory, which contain 400 images with 10 images per each person. The leave one out method is used for validating each pair of extraction and classifier method. The highest accuracy is obtained by a combination of linear kernel and Bi-2DPCA85%, with 94.25%, and also the fastest computation time, is 15.34 seconds. Index Terms— Face Recognition, Principle Component Analysis, Kernel, Support Vector Machine, Leave-one Out Cross Validation

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GOH, LIANG, and NIKOLA KASABOV. "AN INTEGRATED FEATURE SELECTION AND CLASSIFICATION METHOD TO SELECT MINIMUM NUMBER OF VARIABLES ON THE CASE STUDY OF GENE EXPRESSION DATA." Journal of Bioinformatics and Computational Biology 03, no. 05 (October 2005): 1107–36. http://dx.doi.org/10.1142/s0219720005001533.

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This paper introduces a novel generic approach for classification problems with the objective of achieving maximum classification accuracy with minimum number of features selected. The method is illustrated with several case studies of gene expression data. Our approach integrates filter and wrapper gene selection methods with an added objective of selecting a small set of non-redundant genes that are most relevant for classification with the provision of bins for genes to be swapped in the search for their biological relevance. It is capable of selecting relatively few marker genes while giving comparable or better leave-one-out cross-validation accuracy when compared with gene ranking selection approaches. Additionally, gene profiles can be extracted from the evolving connectionist system, which provides a set of rules that can be further developed into expert systems. The approach uses an integration of Pearson correlation coefficient and signal-to-noise ratio methods with an adaptive evolving classifier applied through the leave-one-out method for validation. Datasets of gene expression from four case studies are used to illustrate the method. The results show the proposed approach leads to an improved feature selection process in terms of reducing the number of variables required and an increased in classification accuracy.

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Pang, Zhiyong, Dongmei Zhu, Dihu Chen, Li Li, and Yuanzhi Shao. "A Computer-Aided Diagnosis System for Dynamic Contrast-Enhanced MR Images Based on Level Set Segmentation and ReliefF Feature Selection." Computational and Mathematical Methods in Medicine 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/450531.

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This study established a fully automated computer-aided diagnosis (CAD) system for the classification of malignant and benign masses via breast magnetic resonance imaging (BMRI). A breast segmentation method consisting of a preprocessing step to identify the air-breast interfacing boundary and curve fitting for chest wall line (CWL) segmentation was included in the proposed CAD system. The Chan-Vese (CV) model level set (LS) segmentation method was adopted to segment breast mass and demonstrated sufficiently good segmentation performance. The support vector machine (SVM) classifier with ReliefF feature selection was used to merge the extracted morphological and texture features into a classification score. The accuracy, sensitivity, and specificity measurements for the leave-half-case-out resampling method were 92.3%, 98.2%, and 76.2%, respectively. For the leave-one-case-out resampling method, the measurements were 90.0%, 98.7%, and 73.8%, respectively.

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Ottoboni, Matteo, Luciano Pinotti, Marco Tretola, Carlotta Giromini, Eleonora Fusi, Raffaella Rebucci, Maria Grillo, et al. "Combining E-Nose and Lateral Flow Immunoassays (LFIAs) for Rapid Occurrence/Co-Occurrence Aflatoxin and Fumonisin Detection in Maize." Toxins 10, no. 10 (October 16, 2018): 416. http://dx.doi.org/10.3390/toxins10100416.

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The aim of this study was to evaluate the potential use of an e-nose in combination with lateral flow immunoassays for rapid aflatoxin and fumonisin occurrence/co-occurrence detection in maize samples. For this purpose, 161 samples of corn have been used. Below the regulatory limits, single-contaminated, and co-contaminated samples were classified according to the detection ranges established for commercial lateral flow immunoassays (LFIAs) for mycotoxin determination. Correspondence between methods was evaluated by discriminant function analysis (DFA) procedures using IBM SPSS Statistics 22. Stepwise variable selection was done to select the e-nose sensors for classifying samples by DFA. The overall leave-out-one cross-validated percentage of samples correctly classified by the eight-variate DFA model for aflatoxin was 81%. The overall leave-out-one cross-validated percentage of samples correctly classified by the seven-variate DFA model for fumonisin was 85%. The overall leave-out-one cross-validated percentage of samples correctly classified by the nine-variate DFA model for the three classes of contamination (below the regulatory limits, single-contaminated, co-contaminated) was 65%. Therefore, even though an exhaustive evaluation will require a larger dataset to perform a validation procedure, an electronic nose (e-nose) seems to be a promising rapid/screening method to detect contamination by aflatoxin, fumonisin, or both in maize kernel stocks.

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Huda, Baenil, Shofa Shofia Hilabi, and Maya Rahayuningsih. "Android Based Employee Absence and Leaving Application Information System." Buana Information Technology and Computer Sciences (BIT and CS) 2, no. 1 (January 10, 2021): 11–16. http://dx.doi.org/10.36805/bit-cs.v2i1.1243.

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Attendance in general is the recording of employee attendance and is one aspect of assessment in a company. The purposes of this study are finding out how employees can apply for leave and how to design and build an Android-based attendance system application. Data collection methods that used in this research include observation, interviews and literature study. The system development method that will be used is the System Development Life Cycle (SDLC) model of the Waterfall. With this system, employees and companies will be helped in the problem of absence and leave. The company will get accurate, fast and precise data in decision making.

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Makhatkov, Igor. "On the validation of spatial statistical vegetation models." BIO Web of Conferences 16 (2019): 00020. http://dx.doi.org/10.1051/bioconf/20191600020.

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The features of coefficients of determination and coefficients of leave-one-out method for spatial vegetation model and spatial models of squared deviations are discussed. The properties of models are illustrated in key area for spatial model of Cladonia stellaris projective cover.

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Montoye, Alexander H. K., Bradford S. Westgate, Morgan R. Fonley, and Karin A. Pfeiffer. "Cross-validation and out-of-sample testing of physical activity intensity predictions with a wrist-worn accelerometer." Journal of Applied Physiology 124, no. 5 (May 1, 2018): 1284–93. http://dx.doi.org/10.1152/japplphysiol.00760.2017.

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Wrist-worn accelerometers are gaining popularity for measurement of physical activity. However, few methods for predicting physical activity intensity from wrist-worn accelerometer data have been tested on data not used to create the methods (out-of-sample data). This study utilized two previously collected data sets [Ball State University (BSU) and Michigan State University (MSU)] in which participants wore a GENEActiv accelerometer on the left wrist while performing sedentary, lifestyle, ambulatory, and exercise activities in simulated free-living settings. Activity intensity was determined via direct observation. Four machine learning models (plus 2 combination methods) and six feature sets were used to predict activity intensity (30-s intervals) with the accelerometer data. Leave-one-out cross-validation and out-of-sample testing were performed to evaluate accuracy in activity intensity prediction, and classification accuracies were used to determine differences among feature sets and machine learning models. In out-of-sample testing, the random forest model (77.3–78.5%) had higher accuracy than other machine learning models (70.9–76.4%) and accuracy similar to combination methods (77.0–77.9%). Feature sets utilizing frequency-domain features had improved accuracy over other feature sets in leave-one-out cross-validation (92.6–92.8% vs. 87.8–91.9% in MSU data set; 79.3–80.2% vs. 76.7–78.4% in BSU data set) but similar or worse accuracy in out-of-sample testing (74.0–77.4% vs. 74.1–79.1% in MSU data set; 76.1–77.0% vs. 75.5–77.3% in BSU data set). All machine learning models outperformed the euclidean norm minus one/GGIR method in out-of-sample testing (69.5–78.5% vs. 53.6–70.6%). From these results, we recommend out-of-sample testing to confirm generalizability of machine learning models. Additionally, random forest models and feature sets with only time-domain features provided the best accuracy for activity intensity prediction from a wrist-worn accelerometer. NEW & NOTEWORTHY This study includes in-sample and out-of-sample cross-validation of an alternate method for deriving meaningful physical activity outcomes from accelerometer data collected with a wrist-worn accelerometer. This method uses machine learning to directly predict activity intensity. By so doing, this study provides a classification model that may avoid high errors present with energy expenditure prediction while still allowing researchers to assess adherence to physical activity guidelines.

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Swastika, Windra, Yoshitada Masuda, Rui Xu, Shoji Kido, Yen-Wei Chen, and Hideaki Haneishi. "GND-PCA-Based Statistical Modeling of Diaphragm Motion Extracted from 4D MRI." Computational and Mathematical Methods in Medicine 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/482941.

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We analyzed a statistical model of diaphragm motion using regular principal component analysis (PCA) and generalized N-dimensional PCA (GND-PCA). First, we generate 4D MRI of respiratory motion from 2D MRI using an intersection profile method. We then extract semiautomatically the diaphragm boundary from the 4D-MRI to get subject-specific diaphragm motion. In order to build a general statistical model of diaphragm motion, we normalize the diaphragm motion in time and spatial domains and evaluate the diaphragm motion model of 10 healthy subjects by applying regular PCA and GND-PCA. We also validate the results using the leave-one-out method. The results show that the first three principal components of regular PCA contain more than 98% of the total variation of diaphragm motion. However, validation using leave-one-out method gives up to 5.0 mm mean of error for right diaphragm motion and 3.8 mm mean of error for left diaphragm motion. Model analysis using GND-PCA provides about 1 mm margin of error and is able to reconstruct the diaphragm model by fewer samples.

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Juwitarty, Novita Anggraini, Kosala Dwidja Purnomo, and Kiswara Agung Santoso. "PENDETEKSIAN CITRA DAUN TANAMAN MENGGUNAKAN METODE BOX COUNTING." Majalah Ilmiah Matematika dan Statistika 20, no. 1 (March 16, 2020): 35. http://dx.doi.org/10.19184/mims.v20i1.17221.

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Different types of plants make identification difficult. Therefore, we need a system that can identify the similarity of the leaves based on a reference leaf. Extraction can be done by taking one part of the plant and the most easily obtained part is the leaf part. Natural objects such as leaves have irregular shapes and are difficult to measure, but this can be overcome by using fractal dimensions. In this research, image detection of plant leaves will be carried out using the box counting method. The box counting method is a method of calculating fractal dimensions by dividing images into small boxes in various sizes. Image detection using fractal dimension values, we know which leaves the match with the reference. In this study,10 species of leave were tested, with each species 10 samples of plant leaves. Image testing of plant leaves uses a variety of r box size, namely 1/2 ,1/4 , 1/8 , 1/16 ,1/32 , 1/64 , 128which obtain an average match accuracy of 44%. Keywords: Box Counting, Fractal dimension

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Baillie, M. G. L. "Volcanoes, ice-cores and tree-rings: one story or two?" Antiquity 84, no. 323 (March 1, 2010): 202–15. http://dx.doi.org/10.1017/s0003598x00099877.

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Good archaeology relies on ever more precise dates – obtainable, notably, from ice-cores and dendrochronology. These each provide year-by-year sequences, but they must be anchored at some point to real historical time, by a documented volcanic eruption, for example. But what if the dating methods don't agree? Here the author throws down the gauntlet to the ice-core researchers – their assigned dates are several years too old, probably due to the spurious addition of ‘uncertain’ layers. Leave these out and the two methods correlate exactly…

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Sunarya, Po Abas, Erick Febriyanto, and Jenny Januarini. "Aplikasi Mobile Absensi Karyawan Dan Pengajuan Cuti Berbasis GPS." CCIT Journal 12, no. 2 (August 19, 2019): 241–47. http://dx.doi.org/10.33050/ccit.v12i2.695.

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The use of information technology has become a very influential factor in the company. Information technology is now in the direction of mobile smart devices (mobile / smartphones). One application of information and communication technology used is the development of employee attendance systems by utilizing GPS (Global Positioning System) which is associated with the filing function of employee leave. Employee attendance includes hours of entry and completion of work, while leave includes maternity leave, sick leave, annual leave, and leave for important reasons. The employee attendance and leave application development uses Android operating system based programming used in smartphones. By using the SWOT analysis method (Strength, Weakness, Opportunity, Threat) the problems that occur in the existing system are procedures for approval of attendance and leave which are still carried out outside the existing system. So it still has to ask for verbal approval to the boss after input leave in the system. The alternative problem solving provided aims to maximize the functions of the existing system in order to accommodate the needs of the employee leave procedure. This development aims to make it easier for employees, superiors, and the human resources department to submit, approve and check employee absences and leave

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Pranowo, Harno Dwi, Iqmal Tahir, and Ajidarma Widiatmoko. "QUANTITATIVE RELATIONSHIP OF ELECTRONIC STRUCTURE AND INHIBITION ACTIVITY OF CURCUMIN ANALOGS ON ETHOXYRESORUFIN o-DEALKYLATION (EROD) REACTION." Indonesian Journal of Chemistry 7, no. 1 (June 15, 2010): 78–82. http://dx.doi.org/10.22146/ijc.21717.

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Electronic structure and inhibition activity relationship study of curcumin analogs has been established for 29 curcumin analogs on Ethoxyresorufin O-Dealkylation (EROD) reaction using atomic net charge descriptor based on AM1 semiempirical calculations. The QSAR (Quantitative Structure and Activities Relationships) equation model was determined by statistical parameter from multiple regression analysis and leave-one-out cross validation method. The best QSAR equation was described: Keywords: curcumin, QSAR, descriptor, atomic net charge, semiempirical methods.

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Prabandari, Rani. "Pemberian Kombinasi Ekstrak Etanol Daun Pepaya (carica papaya l.) Pada Mencit." Viva Medika: Jurnal Kesehatan, Kebidanan dan Keperawatan 11, no. 02 (June 13, 2019): 120–30. http://dx.doi.org/10.35960/vm.v11i02.468.

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The natural medicine has been known and used through out of the world. One of them is papaya. Efficacy papaya leave provide efficacy as fever, appetite enhancer, launsed menstruation and relieve pain. This study aims to determine whether the effect of aethanol extract of papaya leaf against power analgesic mefenamic acid on mice by using the method asetic acid induction. This study is an experimental study using twenty four mice. This animals were divided into six groups where each group consisting of four mice. Papaya leaves, mefenamic acid, and aquadest were give to each group as test substances and comparative solution. Observations were carried out for 10 minute using the method of asetic acid induction was a decreases of protection mechanism of the mice in of licking or stiff feet responses after the administration of test substances. There was a decrease of the response of mice to lick feet or stiff feet to the asetic acid induction were given after administration of papaya leaf extract. The extract of papaya leaves has an analgesic effect on mice.

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Arora, Geeta, and Gurpreet Singh Bhatia. "Radial Basis Function Pseudospectral Method for Solving Standard Fitzhugh-Nagumo Equation." International Journal of Mathematical, Engineering and Management Sciences 5, no. 6 (December 1, 2020): 1488–97. http://dx.doi.org/10.33889/ijmems.2020.5.6.110.

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In this article, a pseudospectral approach based on radial basis functions is considered for the solution of the standard Fitzhugh-Nagumo equation. The proposed radial basis function pseudospectral approach is truly mesh free. The standard Fitzhugh-Nagumo equation is approximated into ordinary differential equations with the help of radial kernels. An ODE solver is applied to solve the resultant ODEs. Shape parameter which decides the shape of the radial basis function plays a significant role in the solution. A cross-validation technique which is the extension of the statistical approach leave-one-out-cross-validation is used to find the shape parameter value. The presented method is demonstrated with the help of numerical results which shows a good understanding with the exact solution. The stability of the proposed method is demonstrated with the help of the eigenvalues method numerically.

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Simental-Mendia, Luis E., Matteo Pirro, Stephen L. Atkin, Maciej Banach, Dimitri P. Mikhailidis, and Amirhossein Sahebkar. "Effect of Metformin on Plasma Fibrinogen Concentrations: A Systematic Review and Meta-Analysis of Randomized Placebo-Controlled Trials." Current Pharmaceutical Design 24, no. 9 (May 18, 2018): 1034–40. http://dx.doi.org/10.2174/1381612823666171103165502.

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Objective: Fibrinogen is a key mediator of thrombosis and it has been implicated in the pathogenesis of atherosclerosis. Because metformin has shown a potential protective effect on different atherothrombotic risk factors, we assessed in this meta-analysis its effect on plasma fibrinogen concentrations. Methods: A systematic review and meta-analysis was carried out to identify randomized placebo-controlled trials evaluating the effect of metformin administration on fibrinogen levels. The search included PubMed-Medline, Scopus, ISI Web of Knowledge and Google Scholar databases (by June 2, 2017) and quality of studies was performed according to Cochrane criteria. Quantitative data synthesis was conducted using a random-effects model and sensitivity analysis by the leave-one-out method. Meta-regression analysis was performed to assess the modifiers of treatment response. Results: Meta-analysis of data from 9 randomized placebo-controlled clinical trials with 2302 patients comprising 10 treatment arms did not suggest a significant change in plasma fibrinogen concentrations following metformin therapy (WMD: -0.25 g/L, 95% CI: -0.53, 0.04, p = 0.092). The effect size was robust in the leave-one-out sensitivity analysis and remained non-significant after omission of each single study from the meta-analysis. Conclusion: No significant effect of metformin on plasma fibrinogen concentrations was demonstrated in the current meta-analysis.

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Liu, Peng, Qianbiao Gu, Xiaoli Hu, Xianzheng Tan, Jianbin Liu, An Xie, and Feng Huang. "Applying a radiomics-based strategy to preoperatively predict lymph node metastasis in the resectable pancreatic ductal adenocarcinoma." Journal of X-Ray Science and Technology 28, no. 6 (December 5, 2020): 1113–21. http://dx.doi.org/10.3233/xst-200730.

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PURPOSE: This retrospective study is designed to develop a Radiomics-based strategy for preoperatively predicting lymph node (LN) status in the resectable pancreatic ductal adenocarcinoma (PDAC) patients. METHODS: Eighty-five patients with histopathological confirmed PDAC are included, of which 35 are LN metastasis positive and 50 are LN metastasis negative. Initially, 1,124 radiomics features are computed from CT images of each patient. After a series of feature selection, a Radiomics logistic regression (LOG) model is developed. Subsequently, the predictive efficiency of the model is validated using a leave-one-out cross-validation method. The model performance is evaluated on discrimination and compared with the conventional CT evaluation method based on subjective CT image features. RESULTS: Radiomics LOG model is developed based on eight most related radiomics features. Remarkable differences are demonstrated between patients with LN metastasis positive and LN metastasis negative in Radiomics LOG scores namely, 0.535±1.307 (mean±standard deviation) vs. −1.514±1.800 (mean±standard deviation) with p < 0.001. Radiomics LOG model shows significantly higher predictive efficiency compared to the conventional evaluation method of LN status in which areas under ROC curves are AUC = 0.841 with 95% confidence interval (CI: 0.758∼0.925) vs. AUC = 0.682 with (95% CI: 0.566∼0.798). Leave-one-out cross validation indicates that the Radiomics LOG model correctly classifies 70.3% cases, while the conventional CT evaluation method only correctly classifies 57.0% cases. CONCLUSION: A radiomics-based strategy provides an individualized LN status evaluation in PDAC patients, which may help clinicians implement an optimal personalized patient treatment.

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Yu, Haitao, and Zhiming Dai. "SANPolyA: a deep learning method for identifying Poly(A) signals." Bioinformatics 36, no. 8 (January 6, 2020): 2393–400. http://dx.doi.org/10.1093/bioinformatics/btz970.

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Abstract Motivation Polyadenylation plays a regulatory role in transcription. The recognition of polyadenylation signal (PAS) motif sequence is an important step in polyadenylation. In the past few years, some statistical machine learning-based and deep learning-based methods have been proposed for PAS identification. Although these methods predict PAS with success, there is room for their improvement on PAS identification. Results In this study, we proposed a deep neural network-based computational method, called SANPolyA, for identifying PAS in human and mouse genomes. SANPolyA requires no manually crafted sequence features. We compared our method SANPolyA with several previous PAS identification methods on several PAS benchmark datasets. Our results showed that SANPolyA outperforms the state-of-art methods. SANPolyA also showed good performance on leave-one-motif-out evaluation. Availability and implementation https://github.com/yuht4/SANPolyA. Supplementary information Supplementary data are available at Bioinformatics online.

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Chen, Xuan, Chang Ming Nie, and Song Nian Wen. "QSPR/QSAR Study of Mercaptans by Quantum Topological Method." Advanced Materials Research 233-235 (May 2011): 2536–40. http://dx.doi.org/10.4028/www.scientific.net/amr.233-235.2536.

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A new molecular quantum topological index QT was constructed by molecular topological methods and quantum mechanics (QM), which together with Gibbs free energy(G), Constant volume mole hot melting(CV) that were calculated by density functional theory (DFT) at the B3LYP/6-31G(d) level of theory for mercaptans. Index QT can not only efficiently distinguish molecular structures of mercaptans, but also possess good applications of QSPR/QSAR (quantitative structure-property/activity relationships). And most of the correlation coefficients of the models were over 0.99. The LOO CV (leave-one-out cross-validation) method was used to testify the stability and predictive ability of the models. The validation results verified the good stability and predictive ability of the models employing the cross-validation parameters: RCV, SCVand FCV, which demonstrated the wide potential of the index QT for applications to QSPR/ QSAR.

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Zhalgasuly, N., A. V. Kogut, and A. A. Ismailova. "INVESTIGATION OF LEAVE LEVEL OF COPPER ORE OF ZHEZKAZGAN DEPOSIT." Mining science and technology, no. 2 (August 12, 2018): 14–22. http://dx.doi.org/10.17073/2500-0632-2018-2-14-20.

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In the conditions of the Zhezkazgan copper deposit, which is worked underground, the losses of ore in the left pillars fluctuate from 12 to 25 %, sometimes reaching 40%. During the development of the Zhezkazgan deposit, tens of millions of tons of rich ore were left in various kinds of losses. The annual increase in ore losses in various parts, taking into account the increasing production, is approximately equal to the annual productivity of the whole mine. Also in the production process so far rich in content of the interlayer copper ores of low power. Therefore, the search for the most effective methods of mining lost, off-balance and waste ores is of paramount importance. One of such methods is underground leaching, which allows to carry out their secondary development and make maximum use of the mineral wealth. The aim of the research was to experimentally study the leaching of oxidized, mixed and sulphide copper ores of the old spent mines in the Zhezkazgan deposit using various solvents. The squeezing of oxide and sulphide rudes was carried out in 2 stages, when the durability of the experimental crests was 35 hours and the durable 294 hours. The oxidant-sulphide ore is 20 mm high and can be cured at 50-80 % media, for 10 months. For the period of time, the chalcocin rudus is derived from 30 to 50 % of media, and from 5 to 12 % of bernital chalcopyrite, which results in the effectiveness of the subsequent method of squeezing the effluent. The best dissolves are acidic acid (5-10 g/l) and acidified sulphate oxide (5 g/l). Residual cystic acid production and development of oxidant processes up to 1.6-3.2 t/t for medium oxidized rudder and up to 2.54.1 t/t for chalcocin rudder, which acts as a catalyst for thawing technical and economic indicators.

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Najafi, Amir, Soheil Sobhanardakani, and Mehdi Marjani. "Exploring QSAR for Antimalarial Activities and Drug Distribution within Blood of a Series of 4-Aminoquinoline Drugs Using Genetic-MLR." Journal of Chemistry 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/560415.

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Malaria has been one of the most significant public health problems for centuries. QSAR modeling of the antimalarial activity and blood-to-plasma concentration ratio of Chloroquine and a new series of 4-aminoquinoline derivatives were developed using genetic algorithms with multiple linear regression (GA-MLR) method. We obtained two different models against Chloroquine-sensitive (3D7) and Chloroquine-resistant (W2) strains ofPlasmodium falciparumwith good adjustment levels. Drug distribution in blood, defined as drug blood-to-plasma concentration ratio (Rb), is related to molecular descriptors. Leave-many-out (LMO) andY-randomization methods confirmed the models' robustness.

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Kann, Bonpagna, Thodsaporn Chay-intr, Hour Kaing, and Thanaruk Theeramunkong. "Khmer Treebank Construction via Interactive Tree Visualization." IJITEE (International Journal of Information Technology and Electrical Engineering) 3, no. 3 (December 11, 2019): 67. http://dx.doi.org/10.22146/ijitee.48545.

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Despite the fact that there are a number of researches working on Khmer Language in the field of Natural Language Processing along with some resources regarding words segmentation and POS Tagging, we still lack of high-level resources regarding syntax, Treebanks and grammars, for example. This paper illustrates the semi-automatic framework of constructing Khmer Treebank and the extraction of the Khmer grammar rules from a set of sentences taken from the Khmer grammar books. Initially, these sentences will be manually annotated and processed to generate a number of grammar rules with their probabilities once the Treebank is obtained. In our experiments, the annotated trees and the extracted grammar rules are analyzed in both quantitative and qualitative way. Finally, the results will be evaluated in three evaluation processes including Self-Consistency, 5-Fold Cross-Validation, Leave-One-Out Cross-Validation along with the three validation methods such as Precision, Recall, F1-Measure. According to the result of the three validations, Self-Consistency has shown the best result with more than 92%, followed by the Leave-One-Out Cross-Validation and 5-Fold Cross Validation with the average of 88% and 75% respectively. On the other hand, the crossing bracket data shows that Leave-One-Out Cross Validation holds the highest average with 96% while the other two are 85% and 89%, respectively.

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Wu, Jiaxiang, Guozhao Mai, Bowen Deng, Jeong Younseo, Dongsu Du, Fuxue Chen, and Qiaorong Ma. "Quantitative Structure-activity Relationship of Acetylcholinesterase Inhibitors based on mRMR Combined with Support Vector Regression." Letters in Organic Chemistry 16, no. 4 (March 20, 2019): 311–16. http://dx.doi.org/10.2174/1570178615666181008125341.

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In this work, support vector regression (SVR), an effective machine learning method, proposed by Vapnik was applied to establish QSAR model for a series of AchEI. Fourteen descriptors were selected for constructing the SVR mode by using mRMR-Forward feature selection method. The parameters (ε, C) were adjusted by leave-one-out cross validation (LOOCV) method which was used to judge the predictive power of different models. After optimization, one optimal SVR-QSAR model was attained, and the mean relative errors (MRE) of LOOCV by using SVR is 1.72%. As a result, LogP negatively affected the activity, Refractivity and Water Accessible Surface Area positively affected the activity.

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Zou, Quan, Jinjin Li, Qingqi Hong, Ziyu Lin, Yun Wu, Hua Shi, and Ying Ju. "Prediction of MicroRNA-Disease Associations Based on Social Network Analysis Methods." BioMed Research International 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/810514.

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MicroRNAs constitute an important class of noncoding, single-stranded, ~22 nucleotide long RNA molecules encoded by endogenous genes. They play an important role in regulating gene transcription and the regulation of normal development. MicroRNAs can be associated with disease; however, only a few microRNA-disease associations have been confirmed by traditional experimental approaches. We introduce two methods to predict microRNA-disease association. The first method, KATZ, focuses on integrating the social network analysis method with machine learning and is based on networks derived from known microRNA-disease associations, disease-disease associations, and microRNA-microRNA associations. The other method, CATAPULT, is a supervised machine learning method. We applied the two methods to 242 known microRNA-disease associations and evaluated their performance using leave-one-out cross-validation and 3-fold cross-validation. Experiments proved that our methods outperformed the state-of-the-art methods.

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Lee, Ho-Jin, Hee-Yeon Suh, Yun-Sik Lee, Shin-Jae Lee, Richard E. Donatelli, Calogero Dolce, and Timothy T. Wheeler. "A better statistical method of predicting postsurgery soft tissue response in Class II patients." Angle Orthodontist 84, no. 2 (August 5, 2013): 322–28. http://dx.doi.org/10.2319/050313-338.1.

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ABSTRACT Objective: To propose a better statistical method of predicting postsurgery soft tissue response in Class II patients. Materials and Methods: The subjects comprise 80 patients who had undergone surgical correction of severe Class II malocclusions. Using 228 predictor and 64 soft tissue response variables, we applied two multivariate methods of forming prediction equations, the conventional ordinary least squares (OLS) method and the partial least squares (PLS) method. After fitting the equation, the bias and a mean absolute prediction error were calculated. To evaluate the predictive performance of the prediction equations, a leave-one-out cross-validation method was used. Results: The multivariate PLS method provided a significantly more accurate prediction than the conventional OLS method. Conclusion: The multivariate PLS method was more satisfactory than the OLS method in accurately predicting the soft tissue profile change after surgical correction of severe Class II malocclusions.

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ZHOU, XIN, and K. Z. MAO. "REGULARIZATION NETWORK-BASED GENE SELECTION FOR MICROARRAY DATA ANALYSIS." International Journal of Neural Systems 16, no. 05 (October 2006): 341–52. http://dx.doi.org/10.1142/s0129065706000743.

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Microarray data contains a large number of genes (usually more than 1000) and a relatively small number of samples (usually fewer than 100). This presents problems to discriminant analysis of microarray data. One way to alleviate the problem is to reduce dimensionality of data by selecting important genes to the discriminant problem. Gene selection can be cast as a feature selection problem in the context of pattern classification. Feature selection approaches are broadly grouped into filter methods and wrapper methods. The wrapper method outperforms the filter method but at the cost of more intensive computation. In the present study, we proposed a wrapper-like gene selection algorithm based on the Regularization Network. Compared with classical wrapper method, the computational costs in our gene selection algorithm is significantly reduced, because the evaluation criterion we proposed does not demand repeated training in the leave-one-out procedure.

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Khorasani, Abed, Mohammad Reza Daliri, and Mohammad Pooyan. "Recognition of amyotrophic lateral sclerosis disease using factorial hidden Markov model." Biomedical Engineering / Biomedizinische Technik 61, no. 1 (February 1, 2016): 119–26. http://dx.doi.org/10.1515/bmt-2014-0089.

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Abstract Amyotrophic lateral sclerosis (ALS) is a common disease among neurological disorders that can change the pattern of gait in human. One of the effective methods for recognition and analysis of gait patterns in ALS patients is utilizing stride interval time series. With proper preprocessing for removing unwanted artifacts from the raw stride interval times and then extracting meaningful features from these data, the factorial hidden Markov model (FHMM) was used to distinguish ALS patients from healthy subjects. The results of classification accuracy evaluated using the leave-one-out (LOO) cross-validation algorithm showed that the FHMM method provides better recognition of ALS and healthy subjects compared to standard HMM. Moreover, comparing our method with a state-of-the art method named least square support vector machine (LS-SVM) showed the efficiency of the FHMM in distinguishing ALS subjects from healthy ones.

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Lee, Sang-Jeong, Ji-Yong Yoo, Sung-Keun Yoo, Ryun Ha, Dong-Hyuk Lee, Seon-Tae Kim, and Won-Jin Yi. "Image-Guided Endoscopic Sinus Surgery with 3D Volumetric Visualization of the Nasal Cavity and Paranasal Sinuses: A Clinical Comparative Study." Applied Sciences 11, no. 8 (April 19, 2021): 3675. http://dx.doi.org/10.3390/app11083675.

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(1) Background: The purpose of this study was to develop an image-guided endoscopic sinus surgery (IGESS) system, named Medigator®, based on the leave-one-out registration strategy and three-dimensional (3D) volumetric visualization of the nasal cavity and paranasal sinuses. (2) Methods: A phantom was designed and fabricated using a 3D printer. We then performed a phantom-based accuracy evaluation to validate the performance of the developed registration method. We included 11 patients who underwent IGESS for clinical study to compare the performance of the developed IGESS system with that of a commercialized system. (3) Results: The fiducial registration error (FRE) was 0.14 mm, and the target registration error (TRE) was 0.82 ± 0.50 mm by the phantom-based evaluation. As a result of the clinical comparative study, the average registration times were 36.04 ± 4.7 and 89.35 ± 26.1 s for the developed and commercialized systems, respectively (p < 0.05). The image loading time of the developed system was also shorter than that of the commercialized system (p < 0.05). The average accuracy score of the developed system was not significantly different from that of the commercialized system (p > 0.05). (4) Conclusions: The developed system provided an accurate point-to-point registration method based on the leave-one-out strategy. According to the results of the clinical comparative study, we demonstrated that the developed system showed reliable potential for clinical application.

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Champlin, Velinsky, Tucker, Sommerfield, Laurent, and Watson. "Carbon Sequestration Rate Estimates in Delaware Bay and Barnegat Bay Tidal Wetlands Using Interpolation Mapping." Data 5, no. 1 (January 25, 2020): 11. http://dx.doi.org/10.3390/data5010011.

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Quantifying carbon sequestration by tidal wetlands is important for the management of carbon stocks as part of climate change mitigation. This data publication includes a spatial analysis of carbon accumulation rates in Barnegat and Delaware Bay tidal wetlands. One method calculated long-term organic carbon accumulation rates from radioisotope-dated (Cs-137) sediment cores. The second method measured organic carbon density of sediment accumulated above feldspar marker beds. Carbon accumulation rates generated by these two methods were interpolated across emergent wetland areas, using kriging, with uncertainty estimated by leave-one-out cross validation. This spatial analysis revealed greater carbon sequestration within Delaware, compared to Barnegat Bay. Sequestration rates were found to be more variable within Delaware Bay, and rates were greatest in the tidal freshwater area of the upper bay.

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Li, Qingbo, Wei Wang, Xiaofeng Ling, and Jin Guang Wu. "Detection of Gastric Cancer with Fourier Transform Infrared Spectroscopy and Support Vector Machine Classification." BioMed Research International 2013 (2013): 1–4. http://dx.doi.org/10.1155/2013/942427.

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Early diagnosis and early medical treatments are the keys to save the patients' lives and improve the living quality. Fourier transform infrared (FT-IR) spectroscopy can distinguish malignant from normal tissues at the molecular level. In this paper, programs were made with pattern recognition method to classify unknown samples. Spectral data were pretreated by using smoothing and standard normal variate (SNV) methods. Leave-one-out cross validation was used to evaluate the discrimination result of support vector machine (SVM) method. A total of 54 gastric tissue samples were employed in this study, including 24 cases of normal tissue samples and 30 cases of cancerous tissue samples. The discrimination results of SVM method showed the sensitivity with 100%, specificity with 83.3%, and total discrimination accuracy with 92.2%.

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Journal articles on the topic 'Leave one out method'

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