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An Ensemble Learning Method for Text Classification Based on Heterogeneous Classifiers

International Journal of Advanced Network, Monitoring and Controls
Volume 3 (2018): Issue 1 (January 2018)
By:
Open Access
|May 2018

Figures & Tables

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
DOI: https://doi.org/10.21307/ijanmc-2018-021 | Journal eISSN: 2470-8038
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Language: English
Page range: 130 - 134
Published on: May 7, 2018
Published by: Xi’an Technological University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
Machine Learning,
Ensemble Learning,
Text Classification
Related subjects:
Computer sciences,
Computer sciences, other

© 2018 Fan Huimin, Li Pengpeng, Zhao Yingze, Li Danyang, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution 4.0 License.

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