TITLE:
Lymph Diseases Prediction Using Random Forest and Particle Swarm Optimization
AUTHORS:
Waheeda Almayyan
KEYWORDS:
Classification, Random Forest Ensemble, PSO, Simple Random Sampling, Information Gain Ratio, Symmetrical Uncertainty
JOURNAL NAME:
Journal of Intelligent Learning Systems and Applications,
Vol.8 No.3,
August
3,
2016
ABSTRACT: This research aims to develop a model to
enhance lymphatic diseases diagnosis by the use of random forest ensemble
machine-learning method trained with a simple sampling scheme. This study has
been carried out in two major phases: feature selection and classification. In
the first stage, a number of discriminative features out of 18 were selected
using PSO and several feature selection techniques to reduce the features
dimension. In the second stage, we applied the random forest ensemble
classification scheme to diagnose lymphatic diseases. While making experiments
with the selected features, we used original and resampled distributions of the
dataset to train random forest classifier. Experimental results demonstrate
that the proposed method achieves a remark-able improvement in classification
accuracy rate.