[PDF] Top 20 Statistical Comparisons of Classifiers over Multiple Data Sets
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Statistical Comparisons of Classifiers over Multiple Data Sets
... 14 data sets from the UCI repository with binary class ...two data sets on which the classifiers performed equally (lung-cancer and mushroom); if there was an odd number of them, we ... See full document
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An Extension on “Statistical Comparisons of Classifiers over Multiple Data Sets” for all Pairwise Comparisons
... of classifiers over multiple data ...non-parametric statistical techniques (Zar, 1999; Sheskin, 2003) for comparing classifiers under these circumstances, given that the sample ... See full document
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Empirical aspects of record linkage across multiple data sets using statistical linkage keys: the experience of the PIAC cohort study
... Probabilistic matching allows for variation in reported characteristics by deriving a measure of simi- larity across variables used to identify matches, called the match weight. This is then used to decide whether a ... See full document
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Tropospheric dry layers in the tropical western Pacific: comparisons of GPS radio occultation with multiple data sets
... From the 16 research flights, we extracted 75 profiles that ex- tend over at least 6 km altitude and are within the region 5 ◦ S to 20 ◦ N latitude, 130 to 170 ◦ E longitude (Fig. 1). We tried different criteria ... See full document
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IMPROVE THE ACCURACY OF CLASSIFIERS PERFORMANCE USING MACHINE LEARNING & DATA PREPROCESSED METHODS ON NSL-KDD DATA SETS.
... selected data points nearer to the hyper-plane are said to be a support ...in statistical learning theory projected by Vapnik ...from statistical learning ... See full document
7
Application of Statistical Mirroring in Biological Sequence Analysis
... pairwise, multiple, global and local techniques are the main principles that underpin comparative ...and statistical mirroring was used and adopted to provide a suitable alternative for multiple ... See full document
22
PolyU CBS CFA at the FinSBD Task: Sentence Boundary Detection of Financial Data with Domain Knowledge Enhancement and Bilingual Training
... noisy data in Financial ...the classifiers on parameters of min samples split and max features for RFC and batch size, epochs for NN with optimized per- formance on Dev ...training data. The results ... See full document
8
Evaluation of multiple forcing data sets for precipitation and shortwave radiation over major land areas of China
... each data set for mainland China: the area-weighted average (mean), the standard deviation (SD) and the coefficient of variation ...further comparisons, CMFD and CLDAS are resampled to match the ...GLDAS ... See full document
17
The search for stable prognostic models in multiple imputed data sets
... In the standard statistical methodology the use of a backward or forward selection strategy has been criti- cized. It may result in overoptimistically estimated regression coefficients, omission of important ... See full document
9
INEQUALITIES IN HEALTH STATUS DEPENDING ON SOCIO-ECONOMIC SITUATION IN THE EUROPEAN COUNTRIES
... multidimensional statistical methods should give an answer to the question as to whether and how strongly the socio-economic situation in the countries of Europe affects the health status of the population and how ... See full document
17
Multiple Classifiers System for Medical Diagnosis
... datasets. Data mining in medical field is a challenging task because of the complexity in the medical ...research multiple classifiers system gave the reliability ... See full document
6
Identification of Tannerella Forsythia and Treponema Denticola in Down Syndrome Subjects and Healthy Subjects with Periodontal Disease- A PCR Study
... Each group mean and standard deviation was calculated . Comparing the mean value among different study groups by using one-way ANOVA Kruskal-Wallis followed by post-hoc (Tukey HSD) test. The statistical analysis ... See full document
6
A framework for assessing high school students statistical reasoning
... of statistical reasoning ...students’ statistical thinking levels lie within the four cogni- tive thinking levels of the SOLO ...of statistical reasoning, we assumed that the statistical ... See full document
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Spatial prediction models for landslide hazards: review, comparison and evaluation
... The objective of the present work is to review the present practice of applied spatial landslide hazard modelling as re- flected by the scientific literature, and to compare selected statistical classification ... See full document
10
A Survey of Software Packages Used for Rough Set Analysis
... analysis, data mining and pattern recogni- tion is that all of them deal with imprecise or incomplete ...appropriate data processing tools must be employed when researching computational intelligence and ... See full document
9
Use and Misuse of multiple Comparisons Procedures of Means in Factorials Experiments
... cases multiple comparisons were used, 2 cases applied pair wise multiple comparisons by using LSD for quantitative data, 9 cases applied multiple range test by using Duncan in ... See full document
8
If not multiple comparisons, then what?
... hoc comparisons of most interest using the LSD value, calculated from the relevant stan- dard error of the difference (SED) from ...pairwise comparisons may be ... See full document
5
by Chance Enhancing Interaction with Large Data Sets Through Statistical Sampling
... less data items sampled! Also, we may be more interested in the proportionate error rather than absolute error; an error of 5 pixels in a 10 pixel high histogram is more visually significant than a 5 pixel error ... See full document
10
Influence of degraded visual acuity from light scattering goggles on obstacle gait
... stepping over the obstacles, the two-way ANOVA for repeated measures was used to examine the mean differences among the obstacle heights and visual input ...method. Multiple comparison was also examined by ... See full document
7
Effective Decision Tree Learning
... with data sets containing numerical attributes with point ...training data sets with numerical attributes containing point data for different error values and then decision tree ... See full document
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