An Ensemble Co-Evolutionary based Algorithm for Classification Problems

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.

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Wrapper Feature Subset Selection for Dimension Reduction Based on Ensemble Learning Algorithm

Procedia Computer Science ◽

10.1016/j.procs.2015.12.117 ◽

2015 ◽

Vol 72 ◽

pp. 162-169 ◽

Cited By ~ 32

Author(s):

Rattanawadee Panthong ◽

Anongnart Srivihok

Keyword(s):

Dimension Reduction ◽

Ensemble Learning ◽

Learning Algorithm ◽

Subset Selection ◽

Feature Subset Selection ◽

Feature Subset ◽

Ensemble Learning Algorithm ◽

Selection For

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Rough set based ensemble learning algorithm for agricultural data classification

Filomat ◽

10.2298/fil1805917s ◽

2018 ◽

Vol 32 (5) ◽

pp. 1917-1930 ◽

Cited By ~ 1

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.

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An Improved LS-SVR Ensemble Learning in Internet Traffic Prediction

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.3794 ◽

2011 ◽

Vol 121-126 ◽

pp. 3794-3798 ◽

Cited By ~ 1

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.

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

10.21203/rs.3.rs-1100298/v1 ◽

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.

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Speech Dereverberation Based on Integrated Deep and Ensemble Learning Algorithm

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2018.8462662 ◽

2018 ◽

Cited By ~ 5

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

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Klasifikasi Resiko Kehamilan Menggunakan Ensemble Learning berbasis Classification Tree

INFORMAL: Informatics Journal ◽

10.19184/isj.v6i3.28396 ◽

2021 ◽

Vol 6 (3) ◽

pp. 177

Author(s):

Muhamad Arief Hidayat

Keyword(s):

Ensemble Learning ◽

Classification Tree ◽

Subset Selection ◽

Feature Subset Selection ◽

Feature Subset ◽

Pregnancy Risk ◽

Risk Status ◽

Cost Sensitive Learning ◽

Best Value ◽

Selection Stage

In health science there is a technique to determine the level of risk of pregnancy, namely the Poedji Rochyati score technique. In this evaluation technique, the level of pregnancy risk is calculated from the values of 22 parameters obtained from pregnant women. Under certain conditions, some parameter values are unknown. This causes the level of risk of pregnancy can not be calculated. For that we need a way to predict pregnancy risk status in cases of incomplete attribute values. There are several studies that try to overcome this problem. The research "classification of pregnancy risk using cost sensitive learning" [3] applies cost sensitive learning to the process of classifying the level of pregnancy risk. In this study, the best classification accuracy achieved was 73% and the best value was 77.9%. To increase the accuracy and recall of predicting pregnancy risk status, in this study several improvements were proposed. 1) Using ensemble learning based on classification tree 2) using the SVMattributeEvaluator evaluator to optimize the feature subset selection stage. In the trials conducted using the classification tree-based ensemble learning method and the SVMattributeEvaluator at the feature subset selection stage, the best value for accuracy was up to 76% and the best value for recall was up to 89.5%

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A NEW ENSEMBLE LEARNING ALGORITHM USING REGIONAL CLASSIFIERS

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213013500255 ◽

2013 ◽

Vol 22 (04) ◽

pp. 1350025 ◽

Cited By ~ 3

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.

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