scholarly journals Rough set based ensemble learning algorithm for agricultural data classification

Filomat ◽  
2018 ◽  
Vol 32 (5) ◽  
pp. 1917-1930 ◽  
Author(s):  
Lei Shi ◽  
Qiguo Duan ◽  
Juanjuan Zhang ◽  
Lei Xi ◽  
Hongbo Qiao ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Rough Set ◽  
Rough Set Theory ◽  
Learning Algorithm ◽  
Uncertain Data ◽  
Data Classification ◽  
Research Area ◽  
Experimental Comparison ◽  
Classification Problems ◽  
Ensemble Learning Algorithm

Agricultural data classification attracts more and more attention in the research area of intelligent agriculture. As a kind of important machine learning methods, ensemble learning uses multiple base classifiers to deal with classification problems. The rough set theory is a powerful mathematical approach to process unclear and uncertain data. In this paper, a rough set based ensemble learning algorithm is proposed to classify the agricultural data effectively and efficiently. An experimental comparison of different algorithms is conducted on four agricultural datasets. The results of experiment indicate that the proposed algorithm improves performance obviously.

scholarly journals An Ensemble Co-Evolutionary based Algorithm for Classification Problems

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Vũ Văn Trường ◽  
Bùi Thu Lâm ◽  
Nguyễn Thành Trung
Keyword(s):  
Ensemble Learning ◽  
Learning Algorithm ◽  
Feature Subset Selection ◽  
Learning Approach ◽  
Feature Subset ◽  
Classification Problems ◽  
Classification Result ◽  
Classifier Design ◽  
Optimal Classifier ◽  
Ensemble Learning Algorithm

In this paper, the authors propose a dual-population co-evolutionary approach using ensemble learning approach (E-SOCA)  to  simultaneously  solve  both  feature  subset selection  and  optimal  classifier  design.  Different  from previous  studies  where  each  population  retains  only  one best individual (Elite) after co-evolution, in this study, an elite  community  will  be  stored  and  calculated  together through  an  ensemble  learning  algorithm  to  produce  the final    classification    result.    Experimental    results    on standard  UCI  problems  with  a  variety  of  input  features ranging from small to large sizes shows that the proposed algorithm  results  in  more  accuracy  and  stability  than traditional algorithms.

An Improved LS-SVR Ensemble Learning in Internet Traffic Prediction

2011 ◽  
Vol 121-126 ◽  
pp. 3794-3798 ◽  
Author(s):  
Kun Lun Li ◽  
Ying Hui Ma ◽  
Yong Mei Tian ◽  
Jing Xie
Keyword(s):  
Time Series ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Internet Traffic ◽  
Traffic Prediction ◽  
Initial Training ◽  
Classification Problems ◽  
Traffic Forecasting ◽  
Ensemble Learning Algorithm

In this paper, we present a new method for internet traffic forecasting based on a boosting LS-SVR algorithm. AdaBoost has been proved to be an effective method for improving the performance of weak learning algorithms and widely applied to classification problems. Inspired by it, we use LS-SVR to complete the initial training; and pay more attention on the “high error areas” in the time series; then, we use an ensemble learning algorithm to learn these areas.

BPSO-Adaboost-KNN ensemble learning algorithm for multi-class imbalanced data classification

2016 ◽  
Vol 49 ◽  
pp. 176-193 ◽  
Author(s):  
Guo Haixiang ◽  
Li Yijing ◽  
Li Yanan ◽  
Liu Xiao ◽  
Li Jinling
Keyword(s):  
Ensemble Learning ◽  
Learning Algorithm ◽  
Imbalanced Data ◽  
Data Classification ◽  
Imbalanced Data Classification ◽  
Ensemble Learning Algorithm

Prediction of Software Defect Using Ensemble Learning Based Improved Sparrow Search Algorithm To Optimize Extreme Learning Machine

2021 ◽  
Author(s):  
Yu Tang ◽  
Qi Dai ◽  
Mengyuan Yang ◽  
Lifang Chen
Keyword(s):  
Extreme Learning Machine ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Search Algorithm ◽  
Defect Prediction ◽  
Software Defect Prediction ◽  
Prediction Ability ◽  
Software Defect ◽  
Ensemble Learning Algorithm ◽  
Learning Machine

Abstract For the traditional ensemble learning algorithm of software defect prediction, the base predictor exists the problem that too many parameters are difficult to optimize, resulting in the optimized performance of the model unable to be obtained. An ensemble learning algorithm for software defect prediction that is proposed by using the improved sparrow search algorithm to optimize the extreme learning machine, which divided into three parts. Firstly, the improved sparrow search algorithm (ISSA) is proposed to improve the optimization ability and convergence speed, and the performance of the improved sparrow search algorithm is tested by using eight benchmark test functions. Secondly, ISSA is used to optimize extreme learning machine (ISSA-ELM) to improve the prediction ability. Finally, the optimized ensemble learning algorithm (ISSA-ELM-Bagging) is presented in the Bagging algorithm which improve the prediction performance of ELM in software defect datasets. Experiments are carried out in six groups of software defect datasets. The experimental results show that ISSA-ELM-Bagging ensemble learning algorithm is significantly better than the other four comparison algorithms under the six evaluation indexes of Precision, Recall, F-measure, MCC, Accuracy and G-mean, which has better stability and generalization ability.

scholarly journals Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm

2018 ◽  
Author(s):  
Wei-Jen Lee ◽  
Syu-Siang Wang ◽  
Fei Chen ◽  
Xugang Lu ◽  
Shao-Yi Chien ◽  
...  
Keyword(s):  
Ensemble Learning ◽  
Learning Algorithm ◽  
Speech Dereverberation ◽  
Ensemble Learning Algorithm

A NEW ENSEMBLE LEARNING ALGORITHM USING REGIONAL CLASSIFIERS

2013 ◽  
Vol 22 (04) ◽  
pp. 1350025 ◽  
Author(s):  
BYUNGWOO LEE ◽  
SUNGHA CHOI ◽  
BYONGHWA OH ◽  
JIHOON YANG ◽  
SUNGYONG PARK
Keyword(s):  
Ensemble Learning ◽  
Learning Algorithm ◽  
Ensemble Methods ◽  
Feature Space ◽  
Training Data ◽  
Learning Method ◽  
Weighted Voting ◽  
Ensemble Learning Algorithm ◽  
Voting Scheme ◽  
Base Learner

We present a new ensemble learning method that employs a set of regional classifiers, each of which learns to handle a subset of the training data. We split the training data and generate classifiers for different regions in the feature space. When classifying an instance, we apply a weighted voting scheme among the classifiers that include the instance in their region. We used 11 datasets to compare the performance of our new ensemble method with that of single classifiers as well as other ensemble methods such as RBE, bagging and Adaboost. As a result, we found that the performance of our method is comparable to that of Adaboost and bagging when the base learner is C4.5. In the remaining cases, our method outperformed other approaches.

Evolutionary Ensemble Learning Algorithm to Modeling of Warfarin Dose Prediction for Chinese

2019 ◽  
Vol 23 (1) ◽  
pp. 395-406 ◽  
Author(s):  
Yanyun Tao ◽  
Yenming J. Chen ◽  
Xiangyu Fu ◽  
Bin Jiang ◽  
Yuzhen Zhang
Keyword(s):  
Ensemble Learning ◽  
Learning Algorithm ◽  
Warfarin Dose ◽  
Dose Prediction ◽  
Ensemble Learning Algorithm

Joint spectral-spatial hyperspectral image classification based on hierarchical subspace switch ensemble learning algorithm

2018 ◽  
Vol 48 (11) ◽  
pp. 4128-4148 ◽  
Author(s):  
Yongming Li ◽  
Tingjie Xie ◽  
Pin Wang ◽  
Jie Wang ◽  
Shujun Liu ◽  
...  
Keyword(s):  
Image Classification ◽  
Ensemble Learning ◽  
Hyperspectral Image ◽  
Learning Algorithm ◽  
Hyperspectral Image Classification ◽  
Ensemble Learning Algorithm
Sign in / Sign up

Export Citation Format

Share Document