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Oversampling Method Based on Gaussian Distribution and K-Means Clustering

Computers, Materials & Continua 2021年第10期69卷 451-469页

作者： masoud muhammed hassan Adel Sabry Eesa Ahmed Jameel Mohammed Wahab Kh.Arabo Department of Computer Science University of ZakhoDuhok42001Kurdistan RegionIraq Department of Information Technology Duhok Polytechnic UniversityDuhok42001Kurdistan RegionIraq

Learning from imbalanced data is one of the greatest challenging problems in binary classification,and this problem has gained more importance in recent *** the class distribution is imbalanced,classical machine learning algorithms tend to move strongly towards the majority class and disregard the ***,the accuracy may be high,but the model cannot recognize data instances in the minority class to classify them,leading to many *** methods have been proposed in the literature to handle the imbalance problem,but most are complicated and tend to simulate unnecessary *** this paper,we propose a simple oversampling method based on Multivariate Gaussian distribution and K-means clustering,called *** new method aims to avoid generating noise and control imbalances between and within *** experiments have been carried out with six classifiers and four oversampling *** results on different imbalanced datasets show that the proposed GK-Means outperforms other oversampling methods and improves classification performance as measured by F1-score and Accuracy.

关键词： Class imbalance oversampling gaussian multivariate distribution k-means clustering

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Face Recognition Based on Gabor Feature Extraction Followed by FastICA and LDA

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Computers, Materials & Continua 2021年第8期68卷 1637-1659页

作者： masoud muhammed hassan Haval Ismael Hussein Adel Sabry Eesa Ramadhan J.Mstafa Department of Computer Science Faculty of ScienceUniversity of ZakhoDuhok42002Kurdistan RegionIraq Scientific Research and Development Center Nawroz UniversityDuhok42001Kurdistan RegionIraq

Over the past few decades,face recognition has become the most effective biometric technique in recognizing people’s identity,as it is widely used in many areas of our daily ***,it is a challenging technique since facial images vary in rotations,expressions,and *** minimize the impact of these challenges,exploiting information from various feature extraction methods is recommended since one of the most critical tasks in face recognition system is the extraction of facial ***,this paper presents a new approach to face recognition based on the fusion of Gabor-based feature extraction,Fast Independent Component Analysis(FastICA),and Linear Discriminant Analysis(LDA).In the presented method,first,face images are transformed to grayscale and resized to have a uniform *** that,facial features are extracted from the aligned face image using Gabor,FastICA,and LDA ***,the nearest distance classifier is utilized to recognize the identity of the ***,the performance of six distance classifiers,namely Euclidean,Cosine,Bray-Curtis,Mahalanobis,Correlation,and Manhattan,are *** results revealed that the presented method attains a higher rank-one recognition rate compared to the recent approaches in the literature on four benchmarked face datasets:ORL,GT,FEI,and ***,it showed that the proposed method not only helps in better extracting the features but also in improving the overall efficiency of the facial recognition system.

关键词： Artificial intelligence face recognition FastICA Gabor filter bank LDA

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