univariate filter
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... -- 77 -- 81 -- 84 -- 85 -- 91 -- 98 -- 100 -- 101 -- 104 -- 107 -- 110 -- 114 -- 118 -- 122 -- UNIVARIATE ... ... SEASONAL PATTERNS ESTIMATING SEASONAL PATTERNS - DETECTING SEASONAL PATTERNS -- HODRICK-PRESCOTT FILTER ...
1. elektronické vydání 1 online zdroj (220 stran)
Feature selection is a significant part of many machine learning applications dealing with small-sample and high-dimensional data. Choosing the most important features is an essential step for knowledge discovery in many areas of biomedical informatics. The increased popularity of feature selection methods and their frequent utilisation raise challenging new questions about the interpretability and stability of feature selection techniques. In this study, we compared the behaviour of ten state-of-the-art filter methods for feature selection in terms of their stability, similarity, and influence on prediction performance. All of the experiments were conducted on eight two-class datasets from biomedical areas. While entropy-based feature selection appears to be the most stable, the feature selection techniques yielding the highest prediction performance are minimum redundance maximum relevance method and feature selection based on Bhattacharyya distance. In general, univariate feature selection techniques perform similarly to or even better than more complex multivariate feature selection techniques with high-dimensional datasets. However, with more complex and smaller datasets multivariate methods slightly outperform univariate techniques.
- MeSH
- algoritmy MeSH
- databáze faktografické MeSH
- lidé MeSH
- multivariační analýza MeSH
- Parkinsonova nemoc diagnóza MeSH
- sekvenční analýza hybridizací s uspořádaným souborem oligonukleotidů metody MeSH
- software MeSH
- statistické modely MeSH
- výpočetní biologie metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- srovnávací studie MeSH
... 202 -- 4.4.1 2D Radon transform and its adjoint 202 -- 4.4.2 Central-slice theorem 205 -- 4.4.3 Filtered ... ... functionals 382 -- 8.2.4 Correlation analysis 383 -- 8.2.5 Spectral analysis 389 -- 8.2.6 Linear filtering ... ... boundary conditions 477 -- 9.4.3 Application of Green’s theorem 478 -- 9.4.4 Diffraction as a 2D linear filter ... ... point processes 662 -- 11.3.10 Characteristic functionals of filtered point processes 665 -- 11.3.11 ... ... -- SPECKLE 1285 -- 18.5.1 Object fields and objects 1286 -- 18.5.2 Image fields 1291 -- 18.5.3 Univariate ...
Wiley series in pure and applied optics
[1st ed.] xli, 1540 s. : il.
... -- 4 Controling Monte Carlo Variance 123 -- 4.1 Monitoring Variation with the CLT 123 -- 4.1.1 Univariate ... ... Monte Carlo 547 -- 14.3.2 Hidden Markov Models 549 -- 14.3.3 Weight Degeneracy 551 -- 14.3.4 Particle Filters ...
Springer texts in statistics
2nd ed. xxx, 645 s., grafy
