Description of Data Sets and Sampling Methods
The AMSU-based Hydrological Bundle Climate Data Record – Description and Comparison with Other Data Sets
... similar data sets. First, the majority of this sixteen year data sets contains measurements from different times of the day, and therefore the diurnal cycle can be ...other data ...
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KNOWLEDGE DISCOVERY and SAMPLING TECHNIQUES with DATA MINING for IDENTIFYING TRENDS in DATA SETS
... in data. Pattern in the given data is an expression in interpreting the data or a model applicable to the subset in given ...to data, finding structure from data, or in general any ...
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A Comparison of Re-sampling Techniques for Pattern Classification in Imbalanced Data-Sets
... essential data in more than one feature of the ...dropout methods, there could be some exoneration of over-sampling from over- fitting the classifier ...under-sampling methods did ...
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Sampling Methods for the Nyström Method
... form sampling without replacement, and this remains the sampling method most commonly used in practice (Talwalkar et ...assuming sampling from fixed, non-uniform distributions over the columns ...
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Supervised sampling for clustering large data sets
... the data is proposed, which aims to be robust to the deficiencies of random sub-sampling for clustering ...the data and decide which is the best to use according to some optimality ...of data ...
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Rough Sets in Imbalanced Data Problem: Improving Re sampling Process
... Imbalanced data problem is still one of the most interesting and important research ...rough sets theory to the imbalanced data learning problem could be a promising research direction, the improved ...
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Practical Targeted Learning from Large Data Sets by Survey Sampling
... 1 Introduction Large data sets are ubiquitous nowadays. They pose computational and theoretical challenges. We consider the particular problem of carrying out inference based on semiparametric models by ...
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by Chance Enhancing Interaction with Large Data Sets Through Statistical Sampling
... line data As discussed previously, a key problem with point or line data for very large datasets is when the points or lines saturate the ...simply sampling the data can make these ...till ...
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a priori synthetic sampling for increasing classification sensitivity in imbalanced data sets
... of sampling tech- niques, genetic algorithms and clustering have all been combined to form voting based ensembles used for combating class imbalance [2, 84, 52, 75, 83, ...
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An Improved Sampling Algorithm for Imbalanced Data Sets in the context of Ordinal Classification
... WORK Data samples are one of the most important sources to do research activity especially in data ...balanced data sets. Many real world data samples are not ...educational data ...
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INTEGRATED EFFECT OF DATA CLEANING AND SAMPLING ON DECISION TREE LEARNING OF LARGE DATA SETS
... of data available for data ...The data contains the noise or outlier data to some extent which hampers the classification performance of classifier built on that training ...large data ...
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4. Matrix Methods for Analysis of Structure in Data Sets:
... Copyright Dennis L. Hartmann 2014 1/22/14 9:32 AM 104 chance. Hu (1997) pointed out some lack of uniqueness problems with MCA analysis. 4.12 Canonical Correlation Analysis Principal component analysis and MCA analysis ...
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Logistic regression methods for classification of imbalanced data sets
... classification, data sets are said to be imbalanced, if the number of negative instances are heavily larger than the positive ones (Akbani et ...imbalanced data set, the negative class is the ...
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Parallel Methods for Scaling Data Mining Algorithms to Large Data Sets
... initial data set evenly among the P processors. The processors leave the data in place but move the class distribution information ...the data is then assigned to the children and the process ...
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Taming outliers in pulsar-timing data sets with hierarchical likelihoods and Hamiltonian sampling
... efficient sampling of the hierarchical form of the pulsar-timing ...Such sampling has recently become possible with a “no-U-turn” Hamiltonian sampler coupled to a highly customized reparametrization of the ...
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A SMART SAMPLING SCHEDULING AND SKIPPING SIMULATOR AND ITS EVALUATION ON REAL DATA SETS
... smart sampling algorithm has been proposed to efficiently select and schedule production lots on metrology ...”Smart Sampling Scheduling and Skipping Simulator” ...several sets of instances from ...
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A Review and Comparison of Methods for Detecting Outliers in Univariate Data Sets
... The purpose of this simulation is to present the expected percentage of the observations outside of the interval of several labeling methods according to the sample size and the degree [r] ...
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Fractal Analysis of Time-Series Data Sets: Methods and Challenges
... be made empirically, such as by observing the range of length scales over which the scaling plot is sufficiently linear. Alternatively, this determination may be made by convention, such as may be based on statistical ...
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Sampling Based Range Partition Methods for Big Data Analytics
... the data range par- tition. We consider a simple sampling scheme which assumes a provision to take a random sample of a given sample size from a distributed ...simple sampling scheme without worrying ...
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Sampling Methods for Bats
... This sampling scheme represents a serious compromise between intrastand (number of nights per stand) and interstand (number of stands per treatment group) sampling ...the sampling effort. The ...
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