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An Ensemble Learning Method for Text Classification Based on Heterogeneous Classifiers

Fan Huimin

Fan Huimin

School of Computer Science and Engineering Xi’an Technological UniversityXi’an, China
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Huimin, Fan
, 
Li Pengpeng

Li Pengpeng

School of Computer Science and Engineering Xi’an Technological UniversityXi’an, China
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Pengpeng, Li
, 
Zhao Yingze

Zhao Yingze

School of School of Marxism Xi’an Jiaotong UniversityXi’an, China
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Yingze, Zhao
 y 
Li Danyang

Li Danyang

School of Computer Science and Engineering Xi’an Technological UniversityXi’an, China
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Danyang, Li
  
07 may 2018
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Publicado en línea: 07 may 2018
Páginas: 130 - 134
DOI: https://doi.org/10.21307/ijanmc-2018-021
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© 2018 Fan Huimin et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Experiment on the variation of feature dimension between integrated model and single classifier model
Experiment on the variation of feature dimension between integrated model and single classifier model

TABLE I.

EXPERIMENT OF THE PERTURBATION OF FEATURE SELECTION ALGORITHM
EXPERIMENT OF THE PERTURBATION OF FEATURE SELECTION ALGORITHM

TABLE II.

PARAMETER PERTURBATION EXPERIMENT OF K NEAREST NEIGHBOR CLASSIFIER
PARAMETER PERTURBATION EXPERIMENT OF K NEAREST NEIGHBOR CLASSIFIER

TABLE III.

PERTURBATION EXPERIMENT OF BAYESIAN CLASSIFIER PARAMETER
PERTURBATION EXPERIMENT OF BAYESIAN CLASSIFIER PARAMETER

TABLE IV.

PERTURBATION EXPERIMENT OF LOGISTIC REGRESSION CLASSIFIER PARAMETER
PERTURBATION EXPERIMENT OF LOGISTIC REGRESSION CLASSIFIER PARAMETER

TABLE V.

BASE CLASSIFIER DIVERSITY MEASURE KW VALUE
BASE CLASSIFIER DIVERSITY MEASURE KW VALUE

TABLE VI.

MODEL PARAMETERS
MODEL PARAMETERS

TABLE VII.

THE COMPARISON BETWEEN THE MODEL AND BAGGING MODEL
THE COMPARISON BETWEEN THE MODEL AND BAGGING MODEL
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