Methods to Compare NonParametric Classifiers and to Select the Predictors
| Autor: | Agostino Di Ciaccio, Simone Borra |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2003 |
| Předmět: |
Statistics::Theory
Computer science business.industry Mean squared prediction error Nonparametric statistics Pattern recognition Machine learning computer.software_genre nonparametric classifiers Statistics::Machine Learning ComputingMethodologies_PATTERNRECOGNITION Nonparametric classification Simple (abstract algebra) Evaluation methods variable selection Artificial intelligence Noise (video) Settore SECS-S/01 - Statistica business computer Selection (genetic algorithm) |
| Zdroj: | Studies in Classification, Data Analysis, and Knowledge Organization ISBN: 9783540238096 |
| Popis: | In this paper we examine some nonparametric evaluation methods to compare the prediction capability of supervised classification models. We show also the importance, in nonparametric models, to eliminate the noise variables with a simple selection procedure. It is shown that a simpler model usually gives lower prediction error and is more interpretable. We show some empirical results applying nonparametric classification models on real and artificial data sets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|