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About 10,000 papers for “sample reduction”

Sample reduction affects mAb reactivity.

Gabriele Piconi (6848594), Alexander H. Peden (6848597), Marcelo A. Barria (264036) et al. · Figshare · 2019

<p>Dilutions of FLHa-rPrP of known concentration were boiled with (+) or without (-) 2% reducing agent β-mercaptoethanol. The dilution factor for each mAb is reported. All mAbs were diluted in TBST.</p>

Cited by 0Open access

Gravity Sample Density Reduction Using Gradiometry

James While, E. K. Biegert, William Henry Jackson · 2005

H001 P000 Summary GRAVITY SAMPLE DENSITY REDUCTION USING GRADIOMETRY. J. WHILE 1 E.K. BIEGERT 2 and A. JACKSON 1 1 School of Earth and Environment University of Leeds Leeds LS2 9JT UK. 2 Bellaire Technology Center 3737 Bellaire Blvd. Houston Texas USA. Papoulis’ generalised sampling expansion (Papoulis 1977) is a spectral domain tool that can be used to combine gravimetry with gravity gradiometry to increase the minimum line spacing of a gravity survey without introducing aliasing. We investigated over 50 possible expansions six of which were found to be of use. We discovered that compared with a normal survey we

Cited by 0

Sample volume reduction during the scan.

Federica Spani (8379009), Maria Pia Morigi (8379012), Matteo Bettuzzi (8379015) et al. · Figshare · 2020

<p>Example of volume decrease occurred during the scan of a museum sample (<i>Loris lydekkerianus</i>) verified by comparing radiographic projection n° 0 (A) and n°899 (B) at the beginning and at the end of a tomographic acquisition process (900 radiographic projections acquired in an angular range of 360 degrees). The sample volume variation is shown (C) by overlaying outline of sample at the beginning of the scan (red line) and outline of sample at the end of the scan (yellow line).</p>

Cited by 0Open access

Cost reduction according to sample quantity.

Ligia Traldi Macedo (6458732), Vinicius Eduardo Ferrari (6458735), Juliana Carron (6458738) et al. · Figshare · 2019

<p>Cost reduction (overall cost from standard approach versus genotyping selection) calculations taking into account the number of samples per round.</p>

Cited by 0Open access

Sample Reduction Algorithm Based on Classification Contribution

Zheng Chai, Yanying Li · Research Square · 2022

Abstract Aiming at the disadvantages of exponential growth of time required by KNN algorithm on data sets containing a large number of samples and low classification performance, this paper proposes a sample reduction method based on classification contribution ranking (SRCCR), which not only greatly reduces the storage demand and execution time, but also significantly improves the classification effect of KNN algorithm. Firstly, SRCCR denoises the initial training set, and expands the smooth decision boundary to a certain extent by removing the noise points in the data set; Secondly, the denoised samples are sorted in ascending order based on the classification contribution strategy. The lower the score, the closer the sample is to the class boundary, and the higher the score, the closer it is to the center of the same kind of samples; Finally, the selection process based on local set is used to select a certain representative boundary samples and a small number of central samples to form the final subset. Although the boundary samples contribute the most to the classification accuracy, appropriate retention of central samples can greatly improve the classification effect. In order to verify the effectiveness of the proposed method, we conduct comparative experiments on 24 real data sets from UCI and KEEL databases. Compared with several classical instance selection algorithms, the proposed SRCCR algorithm has certain advantages in accuracy and simplification rate. The results on the two-dimensional data set `Banana' show that SRCCR algorithm not only selects more representative boundary points and center points, but also retains the distribution of the original data set to a great extent.

Cited by 1Open access

Average Total Sample Size and Percentage Reduction.

Haitao Pan (454047), Ping Huang (30439), Zuoren Wang (192732) et al. · Figshare · 2015

<p>Note: The first row is average total sample size in our proposed design; numbers in parentheses are percentages reduction of sample size as compared to the XJT and the three-stage designs.</p>

Cited by 0Open access

A geometric density-based sample reduction method

Mehdi Mohammadi, Bijan Raahemi, Ahmad Akbari · Intelligent Data Analysis · 2015

Analysis of network traffic, financial transactions, and mobile communications are examples of applications where examining entire samples of a large dataset is computationally expensive, and requires significant memory space. A common approach to address this challenge is to reduce the number of s amples without compromising the accuracy of analyzing them. In this paper, we propose a new cluster-based sample reduction method which is unsupervised, geometric, and density-based. The original data is initially divided into clusters, and each cluster is divided into ``portions'' defined as the areas between two concentric circles. Then, using the proposed geometric-based formulas, the selection value of each sample belonging to a specific portion is calculated. Samples are then selected from the original data according to the corresponding calculated selection value. The performance of the proposed method is measured on various datasets and compared with several cluster-based and density-based methods. We conduct various experiments on the NSL-KDD, KDDCup99, and IUSTsip datasets, and evaluate the performance of the proposed method by measuring the cluster validity indices, as well as the accuracy of the classifier applied on the reduced data. We demonstrate that the reduced dataset has similar sample scattering as that of the original dataset. We also demonstrate that, while reducing the sample size of the input dataset in half, the classification accuracy is not reduced significantly, indicating that the proposed method selects the most relevant samples from the original dataset.

Cited by 2

Sample Reduction Strategies for Protein Secondary Structure Prediction

Sema Atasever, Zafer Aydın, Hasan Erbay et al. · Applied Sciences · 2019

Predicting the secondary structure from protein sequence plays a crucial role in estimating the 3D structure, which has applications in drug design and in understanding the function of proteins. As new genes and proteins are discovered, the large size of the protein databases and datasets that can be used for training prediction models grows considerably. A two-stage hybrid classifier, which employs dynamic Bayesian networks and a support vector machine (SVM) has been shown to provide state-of-the-art prediction accuracy for protein secondary structure prediction. However, SVM is not efficient for large datasets due to the quadratic optimization involved in model training. In this paper, two techniques are implemented on CB513 benchmark for reducing the number of samples in the train set of the SVM. The first method randomly selects a fraction of data samples from the train set using a stratified selection strategy. This approach can remove approximately 50% of the data samples from the train set and reduce the model training time by 73.38% on average without decreasing the prediction accuracy significantly. The second method clusters the data samples by a hierarchical clustering algorithm and replaces the train set samples with nearest neighbors of the cluster centers in order to improve the training time. To cluster the feature vectors, the hierarchical clustering method is implemented, for which the number of clusters and the number of nearest neighbors are optimized as hyper-parameters by computing the prediction accuracy on validation sets. It is found that clustering can reduce the size of the train set by 26% without reducing the prediction accuracy. Among the clustering techniques Ward’s method provided the best accuracy on test data.

Cited by 4Open access

Noise reduction in the six-sample training data.

Norio Tanaka (4371805), Akihisa Takahara (8218869), Taichi Hagio (8218872) et al. · Figshare · 2020

<p>We show changes in the features before and after noise filtration for each sample. (A) Venn diagram of somatic SNVs/indels in the HyperPlus- and SureSelect-treated samples after noise filtering. Magenta and green lines indicate HyperPlus and SureSelect treatments. Region [a] (red) indicates somatic SNVs/indels detected in the HyperPlus- but not the SureSelect-treated samples, whereas region [b] (blue) indicates those shared by both treated samples. The features shown in (A) and (B) were extracted from BAM files from the HyperPlus libraries. (B) Dot plots for changes in the SNV/indel number for each sample after noise reduction. (C) Frequency of SNVs per length of palindrome before and after filtering. Categories [a] and [b] are shown on the left and right panels. Faded and full shading indicate before and after filtration. The frequency of SNVs from all six samples is shown as bar plots per odd length of palindrome, in which a somatic SNV was detected at the center (SCP: SNV-centered palindrome). The number on the <i>x</i>-axis indicates the total length of the palindrome. A length “zero” indicates a sequence that lacks a palindrome. (D) Number of filtered palindromes in a histogram. The number of SCPs was counted per odd-number-length palindrome and shown. Gray and black colors indicate one time and recurrently detected palindromes across the samples, respectively. The reference allele sequence was used to count the SCPs. (E) Comparisons of several features in the sequenced results between the HyperPlus and SureSelect treatments after filtering the HyperPlus artifacts. Note that the noise filter was applied to the HyperPlus data but not to the SureSelect data. Left panel: Bar plots for number of SNVs/indels. Middle panel: Frequency of SNVs per length of palindrome. The frequency of SNVs from all samples (<i>n</i> = 6) is shown as bar plots per odd length of palindrome, in which a somatic SNV was detected at the center. The number on the <i>x</i>-axis indicates the total length of the palindrome. Note that a length “zero” indicates a sequence that lacks a palindrome. Right panel: Mutational signature heatmap. The heatmap represents the proportions of 30 COSMIC mutational signatures [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0227427#pone.0227427.ref009" target="_blank">9</a>] computed from trinucleotide frequencies of nucleotide substitutions in each sample. The number above the heatmap indicates each signature computed according to the definitions in the COSMIC database. Sample IDs are shown on the left.</p>

Cited by 0Open access

Heuristic sample reduction based support vector regression method

Yu Hui, Sun Wenzhu, Zhou Xiuzhi et al. · 2016

Support vector regression (SVR) has become one of the most promising methods for function approximation and regression estimation. However, SVR has a time complexity of O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) and a space complexity of O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). When dealing with very large sizes of training sets, SVR takes a lot computational time. To solve this problem, a method called heuristic sample reduction (HSR) is proposed for obtaining a reduced training set that is manageable for SVR. HSR maintains the regression accuracy of SVR by building the reduced training set heuristically with the samples selected from the original. The experimental result shows that the method is very effective.

Cited by 4

METHOD FOR REDUCTION OF CYTOTOXICITY OF SAMPLE CONCENTRATES

Dr. Robert, S. Safferman · 2014

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx The following is an electronic text form of CHAPTER 8 of the USEPA MANUAL OF METHODS FOR VIROLOGY- EPA publication EPA/600/4-84/013 (R-8). The hardcopy form of this chapter can be obtained by contacting:

Cited by 0

Sample Reduction for SVMs via Data Structure Analysis

Defeng Wang, Daniel Yeung, C. C. Tsang Eric · 2007

This paper presents a new sample reduction algorithm, sample reduction by data structure analysis (SR-DSA), for SVMs to improve their scalability. SR-DSA utilizes data structure information in determining which data points are not useful in learning the separating plane and could be removed. As this algorithm is performed before SVMs training, it avoids the problem suffered by most sample reduction methods whose choices of samples heavily depend on repeatedly training of SVMs. Experiments on both synthetic and real world datasets have shown that SR-DSA is capable of reducing the number of samples as well as the time for SVMs training while maintaining high testing accuracy.

Cited by 4