A Hybrid Evolutionary Approach To Construct Optimal Decision Trees With Large Data Sets

2006 ◽  
Author(s):  
D. V. Patil ◽  
R. S. Bichkar
Keyword(s):  
Decision Trees ◽  
Large Data ◽  
Large Data Sets ◽  
Evolutionary Approach ◽  
Optimal Decision ◽  
Data Sets

scholarly journals INTEGRATED EFFECT OF DATA CLEANING AND SAMPLING ON DECISION TREE LEARNING OF LARGE DATA SETS

2014 ◽  
pp. 215-223
Author(s):  
Dipak V. Patil ◽  
Rajankumar S. Bichkar
Keyword(s):  
Decision Trees ◽  
Data Cleaning ◽  
Large Data ◽  
Classification Performance ◽  
Large Data Sets ◽  
Optimal Decision ◽  
Data Sets ◽  
Data Set ◽  
Outlier Data ◽  
Reduced Data

The advances and use of technology in all walks of life results in tremendous growth of data available for data mining. Large amount of knowledge available can be utilized to improve decision-making process. The data contains the noise or outlier data to some extent which hampers the classification performance of classifier built on that training data. The learning process on large data set becomes very slow, as it has to be done serially on available large datasets. It has been proved that random data reduction techniques can be used to build optimal decision trees. Thus, we can integrate data cleaning and data sampling techniques to overcome the problems in handling large data sets. In this proposed technique outlier data is first filtered out to get clean data with improved quality and then random sampling technique is applied on this clean data set to get reduced data set. This reduced data set is used to construct optimal decision tree. Experiments performed on several data sets proved that the proposed technique builds decision trees with enhanced classification accuracy as compared to classification performance on complete data set. Due to use of classification filter a quality of data is improved and sampling reduces the size of the data set. Thus, the proposed method constructs more accurate and optimal sized decision trees and it also avoids problems like overloading of memory and processor with large data sets. In addition, the time required to build a model on clean data is significantly reduced providing significant speedup.

scholarly journals Decision Trees in Large Data Sets

2021 ◽  
Vol 13 (1) ◽  
pp. 140-151
Author(s):  
Zeynep ÇETİNKAYA ◽  
Fahrettin HORASAN
Keyword(s):  
Decision Trees ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

1992 ◽  
Vol 50 (2) ◽  
pp. 1488-1489
Author(s):  
Thomas W. Shattuck ◽  
James R. Anderson ◽  
Neil W. Tindale ◽  
Peter R. Buseck
Keyword(s):  
Cluster Analysis ◽  
Chemical Reactivity ◽  
Large Data ◽  
Large Data Sets ◽  
Particle Analysis ◽  
Data Sets ◽  
Halogen Chemistry ◽  
Complete Study ◽  
Components Analysis ◽  
Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

NVESTIGATION OF THE EFFICIENCY OF DISTRIBUTED INFORMATION SYSTEMS BASED ON THE PROCESSING OF LARGE AMOUNTS OF DATA

2019 ◽  
Author(s):  
Mykhajlo Klymash ◽  
Olena Hordiichuk — Bublivska ◽  
Ihor Tchaikovskyi ◽  
Oksana Urikova
Keyword(s):  
Distributed Systems ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Decomposition ◽  
Distributed Information ◽  
Software Model ◽  
Computing Performance ◽  
Mapreduce Model ◽  
Singular Data

In this article investigated the features of processing large arrays of information for distributed systems. A method of singular data decomposition is used to reduce the amount of data processed, eliminating redundancy. Dependencies of com­putational efficiency on distributed systems were obtained using the MPI messa­ging protocol and MapReduce node interaction software model. Were analyzed the effici­ency of the application of each technology for the processing of different sizes of data: Non — distributed systems are inefficient for large volumes of information due to low computing performance. It is proposed to use distributed systems that use the method of singular data decomposition, which will reduce the amount of information processed. The study of systems using the MPI protocol and MapReduce model obtained the dependence of the duration calculations time on the number of processes, which testify to the expediency of using distributed computing when processing large data sets. It is also found that distributed systems using MapReduce model work much more efficiently than MPI, especially with large amounts of data. MPI makes it possible to perform calculations more efficiently for small amounts of information. When increased the data sets, advisable to use the Map Reduce model.

Using Large Data Sets and Statistical Tools to Define “Failure” and Demonstrate the Safety of DPR

2018 ◽  
Vol 2018 (6) ◽  
pp. 38-39
Author(s):  
Austa Parker ◽  
Yan Qu ◽  
David Hokanson ◽  
Jeff Soller ◽  
Eric Dickenson ◽  
...  
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Statistical Tools
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