Comparing the Behavior of Oversampling and Undersampling Approach of Class Imbalance Learning by Combining Class Imbalance Problem with Noise

2017 ◽  
pp. 23-30 ◽  
Author(s):  
Prabhjot Kaur ◽  
Anjana Gosain
Keyword(s):  
Class Imbalance ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Imbalance Learning ◽  
Class Imbalance Learning

scholarly journals SMOTEMultiBoost: Leveraging the SMOTE with MultiBoost to Confront the Class Imbalance in Supervised Learning

2020 ◽  
Author(s):  
Naveed Ahmad Khan Jhamat ◽  
Ghulam Mustafa ◽  
Zhendong Niu
Keyword(s):  
False Negative ◽  
Class Imbalance ◽  
Sampling Technique ◽  
Classification Algorithms ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Limiting Error ◽  
Improved Performance ◽  
Imbalance Learning ◽  
Class Imbalance Learning

Class imbalance problem is being manifoldly confronted by researchers due to the increasing amount of complicated data. Common classification algorithms are impoverished to perform effectively on imbalanced datasets. Larger class cases typically outbalance smaller class cases in class imbalance learning. Common classification algorithms raise larger class performance owing to class imbalance in data and overall improvement in accuracy as their goal while lowering performance on smaller class. Furthermore, these algorithms deal false positive and false negative in an even way and regard equal cost of misclassifying cases. Meanwhile, different ensemble solutions have been proposed over the years for class imbalance learning but these approaches hamper the performance of larger class as emphasizing on the small class cases. The intuition of this overall degraded outcome would be the low diversity in ensemble solutions and overfitting or underfitting in data resampling techniques. To overcome these problems, we suggest a hybrid ensemble method by leveraging MultiBoost ensemble and Synthetic Minority Over-sampling TEchnique (SMOTE). Our suggested solution leverage the effectiveness of its elements. Therefore, it improves the outcome of the smaller class by reinforcing its space and limiting error in prediction. The proposed method shows improved performance as compare to numerous other algorithms and techniques in experiments.  

Class Imbalance Learning to Heterogeneous Cross Software Projects Defect Prediction

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Research Work ◽  
Class Imbalance ◽  
Training Dataset ◽  
Software Projects ◽  
Class Imbalance Problem ◽  
Software Application ◽  
Imbalance Problem ◽  
Under Sampling ◽  
Imbalance Learning ◽  
Class Imbalance Learning

Heterogeneous CPDP (HCPDP) attempts to forecast defects in a software application having insufficient previous defect data. Nonetheless, with a Class Imbalance Problem (CIP) perspective, one should have a clear view of data distribution in the training dataset otherwise the trained model would lead to biased classification results. Class Imbalance Learning (CIL) is the method of achieving an equilibrium ratio between two classes in imbalanced datasets. There are a range of effective solutions to manage CIP such as resampling techniques like Over-Sampling (OS) & Under-Sampling (US) methods. The proposed research work employs Synthetic Minority Oversampling TEchnique (SMOTE) and Random Under Sampling (RUS) technique to handle CIP. In addition to this, the paper proposes a novel four-phase HCPDP model and contrasts the efficiency of basic HCPDP model with CIP and after handling CIP using SMOTE & RUS with three prediction pairs. Results show that training performance with SMOTE is substantially improved but RUS displays variations in relation to HCPDP for all three prediction pairs.

scholarly journals A New Diversity Technique for Imbalance Learning Ensembles

2018 ◽  
Vol 7 (2.14) ◽  
pp. 478 ◽  
Author(s):  
Hartono . ◽  
Opim Salim Sitompul ◽  
Erna Budhiarti Nababan ◽  
Tulus . ◽  
Dahlan Abdullah ◽  
...  
Keyword(s):  
Hybrid Approach ◽  
Class Imbalance ◽  
Machine Learning Techniques ◽  
Classifier Ensembles ◽  
Classification Problems ◽  
Class Imbalance Problem ◽  
Weighting Method ◽  
Imbalance Problem ◽  
Learning Ensembles ◽  
Imbalance Learning

Data mining and machine learning techniques designed to solve classification problems require balanced class distribution. However, in reality sometimes the classification of datasets indicates the existence of a class represented by a large number of instances whereas there are classes with far fewer instances. This problem is known as the class imbalance problem. Classifier Ensembles is a method often used in overcoming class imbalance problems. Data Diversity is one of the cornerstones of ensembles. An ideal ensemble system should have accurrate individual classifiers and if there is an error it is expected to occur on different objects or instances. This research will present the results of overview and experimental study using Hybrid Approach Redefinition (HAR) Method in handling class imbalance and at the same time expected to get better data diversity. This research will be conducted using 6 datasets with different imbalanced ratios and will be compared with SMOTEBoost which is one of the Re-Weighting method which is often used in handling class imbalance. This study shows that the data diversity is related to performance in the imbalance learning ensembles and the proposed methods can obtain better data diversity.  

scholarly journals Detection of Myocardial Infarction Using ECG and Multi-Scale Feature Concatenate

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1906
Author(s):  
Jia-Zheng Jian ◽  
Tzong-Rong Ger ◽  
Han-Hua Lai ◽  
Chi-Ming Ku ◽  
Chiung-An Chen ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Network Structure ◽  
Class Imbalance ◽  
Class Imbalance Problem ◽  
Multi Scale ◽  
Imbalance Problem ◽  
Average Accuracy ◽  
Significant Difference ◽  
Electrocardiogram Ecg

Diverse computer-aided diagnosis systems based on convolutional neural networks were applied to automate the detection of myocardial infarction (MI) found in electrocardiogram (ECG) for early diagnosis and prevention. However, issues, particularly overfitting and underfitting, were not being taken into account. In other words, it is unclear whether the network structure is too simple or complex. Toward this end, the proposed models were developed by starting with the simplest structure: a multi-lead features-concatenate narrow network (N-Net) in which only two convolutional layers were included in each lead branch. Additionally, multi-scale features-concatenate networks (MSN-Net) were also implemented where larger features were being extracted through pooling the signals. The best structure was obtained via tuning both the number of filters in the convolutional layers and the number of inputting signal scales. As a result, the N-Net reached a 95.76% accuracy in the MI detection task, whereas the MSN-Net reached an accuracy of 61.82% in the MI locating task. Both networks give a higher average accuracy and a significant difference of p < 0.001 evaluated by the U test compared with the state-of-the-art. The models are also smaller in size thus are suitable to fit in wearable devices for offline monitoring. In conclusion, testing throughout the simple and complex network structure is indispensable. However, the way of dealing with the class imbalance problem and the quality of the extracted features are yet to be discussed.

scholarly journals A Novel Focal Phi Loss for Power Line Segmentation with Auxiliary Classifier U-Net

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2803
Author(s):  
Rabeea Jaffari ◽  
Manzoor Ahmed Hashmani ◽  
Constantino Carlos Reyes-Aldasoro
Keyword(s):  
Loss Function ◽  
Class Imbalance ◽  
Power Line ◽  
Aerial Images ◽  
Class Imbalance Problem ◽  
Trade Off ◽  
Urban Scenes ◽  
Imbalance Problem ◽  
A Minor ◽  
Evaluation Parameters

The segmentation of power lines (PLs) from aerial images is a crucial task for the safe navigation of unmanned aerial vehicles (UAVs) operating at low altitudes. Despite the advances in deep learning-based approaches for PL segmentation, these models are still vulnerable to the class imbalance present in the data. The PLs occupy only a minimal portion (1–5%) of the aerial images as compared to the background region (95–99%). Generally, this class imbalance problem is addressed via the use of PL-specific detectors in conjunction with the popular class balanced cross entropy (BBCE) loss function. However, these PL-specific detectors do not work outside their application areas and a BBCE loss requires hyperparameter tuning for class-wise weights, which is not trivial. Moreover, the BBCE loss results in low dice scores and precision values and thus, fails to achieve an optimal trade-off between dice scores, model accuracy, and precision–recall values. In this work, we propose a generalized focal loss function based on the Matthews correlation coefficient (MCC) or the Phi coefficient to address the class imbalance problem in PL segmentation while utilizing a generic deep segmentation architecture. We evaluate our loss function by improving the vanilla U-Net model with an additional convolutional auxiliary classifier head (ACU-Net) for better learning and faster model convergence. The evaluation of two PL datasets, namely the Mendeley Power Line Dataset and the Power Line Dataset of Urban Scenes (PLDU), where PLs occupy around 1% and 2% of the aerial images area, respectively, reveal that our proposed loss function outperforms the popular BBCE loss by 16% in PL dice scores on both the datasets, 19% in precision and false detection rate (FDR) values for the Mendeley PL dataset and 15% in precision and FDR values for the PLDU with a minor degradation in the accuracy and recall values. Moreover, our proposed ACU-Net outperforms the baseline vanilla U-Net for the characteristic evaluation parameters in the range of 1–10% for both the PL datasets. Thus, our proposed loss function with ACU-Net achieves an optimal trade-off for the characteristic evaluation parameters without any bells and whistles. Our code is available at Github.

scholarly journals Hybridization of ring theory-based evolutionary algorithm and particle swarm optimization to solve class imbalance problem

2021 ◽  
Author(s):  
Sayan Surya Shaw ◽  
Shameem Ahmed ◽  
Samir Malakar ◽  
Laura Garcia-Hernandez ◽  
Ajith Abraham ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Real Life ◽  
Class Imbalance ◽  
Ring Theory ◽  
Class Imbalance Problem ◽  
Minority Class ◽  
Swarm Optimization ◽  
Imbalance Problem ◽  
Representative Samples ◽  
Selection Of

AbstractMany real-life datasets are imbalanced in nature, which implies that the number of samples present in one class (minority class) is exceptionally less compared to the number of samples found in the other class (majority class). Hence, if we directly fit these datasets to a standard classifier for training, then it often overlooks the minority class samples while estimating class separating hyperplane(s) and as a result of that it missclassifies the minority class samples. To solve this problem, over the years, many researchers have followed different approaches. However the selection of the true representative samples from the majority class is still considered as an open research problem. A better solution for this problem would be helpful in many applications like fraud detection, disease prediction and text classification. Also, the recent studies show that it needs not only analyzing disproportion between classes, but also other difficulties rooted in the nature of different data and thereby it needs more flexible, self-adaptable, computationally efficient and real-time method for selection of majority class samples without loosing much of important data from it. Keeping this fact in mind, we have proposed a hybrid model constituting Particle Swarm Optimization (PSO), a popular swarm intelligence-based meta-heuristic algorithm, and Ring Theory (RT)-based Evolutionary Algorithm (RTEA), a recently proposed physics-based meta-heuristic algorithm. We have named the algorithm as RT-based PSO or in short RTPSO. RTPSO can select the most representative samples from the majority class as it takes advantage of the efficient exploration and the exploitation phases of its parent algorithms for strengthening the search process. We have used AdaBoost classifier to observe the final classification results of our model. The effectiveness of our proposed method has been evaluated on 15 standard real-life datasets having low to extreme imbalance ratio. The performance of the RTPSO has been compared with PSO, RTEA and other standard undersampling methods. The obtained results demonstrate the superiority of RTPSO over state-of-the-art class imbalance problem-solvers considered here for comparison. The source code of this work is available in https://github.com/Sayansurya/RTPSO_Class_imbalance.

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