An Imbalanced Data Classification Method Based on Multi-Kernel Extreme Learning Machine Fusion

Many real world data is imbalanced, i.e. one category contains significantly more samples than other categories. Traditional classification methods take different categories equally and are often ineffective. Based on the comprehensive analysis of existing researches, we propose a new imbalanced data classification method based on clustering. The method clusters both majority class and minority class at first. Then, clustered minority class will be over-sampled by SMOTE while clustered majority class be under-sampled randomly. Through clustering, the proposed method can avoid the loss of useful information while resampling. Experiments on several UCI datasets show that the proposed method can effectively improve the classification results on imbalanced data.

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A Hybridized Levy Flight Fruit Fly Optimization Based Kernel Extreme Learning Machine for BioMedical Data Classification

2020 International Conference on Artificial Intelligence and Signal Processing (AISP) ◽

10.1109/aisp48273.2020.9073572 ◽

2020 ◽

Author(s):

Pournamasi Parhi ◽

Jyotirmayee Naik ◽

Sthita Prajna Mishra ◽

Ranjeeta Bisoi

Keyword(s):

Extreme Learning Machine ◽

Data Classification ◽

Fruit Fly ◽

Biomedical Data ◽

Lévy Flight ◽

Levy Flight ◽

Fruit Fly Optimization ◽

Kernel Extreme Learning Machine ◽

Learning Machine

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An Ensemble Learning Imbalanced Data Classification Method Based on Sample Combination Optimization

Journal of Physics Conference Series ◽

10.1088/1742-6596/1284/1/012035 ◽

2019 ◽

Vol 1284 ◽

pp. 012035

Author(s):

Yuxin Wang

Keyword(s):

Ensemble Learning ◽

Imbalanced Data ◽

Data Classification ◽

Classification Method ◽

Combination Optimization ◽

Imbalanced Data Classification

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Efficient Kernel Extreme Learning Machine and Neutrosophic C-means-based Attribute Weighting Method for Medical Data Classification

Journal of Circuits System and Computers ◽

10.1142/s0218126620502606 ◽

2020 ◽

Vol 29 (16) ◽

pp. 2050260 ◽

Cited By ~ 1

Author(s):

D. Shiny Irene ◽

T. Sethukarasi

Keyword(s):

Extreme Learning Machine ◽

Data Classification ◽

Medical Data ◽

Integrated System ◽

Weighting Method ◽

Attribute Weighting ◽

Kernel Extreme Learning Machine ◽

Medical Data Classification ◽

Learning Machine

This paper proposes an integrated system neutrosophic C-means-based attribute weighting-kernel extreme learning machine (NCMAW-KELM) for medical data classification using NCM clustering and KELM. To do that, NCMAW is developed, and then combined with classification method in classification of medical data. The proposed approach contains two steps. In the first step, input attributes are weighted using NCMAW method. The purpose of the weighting method is twofold: (i) to improve the classification performance in the classification of the medical data, (ii) to transform from nonlinearly separable dataset to linearly separable dataset. Finally, KELM algorithm is used for medical data classification purpose. In KELM algorithm, four types of kernels, such as Polynomial, Sigmoid, Radial basis function and Linear, are used. The simulation result on our three datasets demonstrates that the sigmoid kernel is outperformed to ELM in most cases. From the results, NCMAW-KELM approach may be a promising method in medical data classification problem.

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