SYMBOLIC ONE-CLASS LEARNING FROM IMBALANCED DATASETS: APPLICATION IN MEDICAL DIAGNOSIS

2009 ◽  
Vol 18 (02) ◽  
pp. 273-309 ◽  
Author(s):  
LUIS MENA ◽  
JESUS A. GONZALEZ
Keyword(s):  
Medical Diagnosis ◽  
Characteristic Curve ◽  
Geometric Mean ◽  
Class Imbalance ◽  
Class Imbalance Problem ◽  
Imbalanced Datasets ◽  
Minority Class ◽  
Imbalance Problem ◽  
Quantitative Metrics ◽  
Medical Dataset

When working with real-world applications we often find imbalanced datasets, those for which there exists a majority class with normal data and a minority class with abnormal or important data. In this work, we make an overview of the class imbalance problem; we review consequences, possible causes and existing strategies to cope with the inconveniences associated to this problem. As an effort to contribute to the solution of this problem, we propose a new rule induction algorithm named Rule Extraction for MEdical Diagnosis (REMED), as a symbolic one-class learning approach. For the evaluation of the proposed method, we use different medical diagnosis datasets taking into account quantitative metrics, comprehensibility, and reliability. We performed a comparison of REMED versus C4.5 and RIPPER combined with over-sampling and cost-sensitive strategies. This empirical analysis of the REMED algorithm showed it to be quantitatively competitive with C4.5 and RIPPER in terms of the area under the Receiver Operating Characteristic curve (AUC) and the geometric mean, but overcame them in terms of comprehensibility and reliability. Results of our experiments show that REMED generated rules systems with a larger degree of abstraction and patterns closer to well-known abnormal values associated to each considered medical dataset.

Using Ensembles for Class-Imbalance Problem to Predict Maintainability of Open Source Software

2020 ◽  
Vol 27 (05) ◽  
pp. 2040011
Author(s):  
Ruchika Malhotra ◽  
Kusum Lata
Keyword(s):  
Software Maintenance ◽  
Software Metrics ◽  
Performance Metrics ◽  
Class Imbalance ◽  
Maintenance Cost ◽  
Class Imbalance Problem ◽  
Imbalanced Datasets ◽  
Minority Class ◽  
Imbalance Problem ◽  
Ensemble Techniques

To facilitate software maintenance and save the maintenance cost, numerous machine learning (ML) techniques have been studied to predict the maintainability of software modules or classes. An abundant amount of effort has been put by the research community to develop software maintainability prediction (SMP) models by relating software metrics to the maintainability of modules or classes. When software classes demanding the high maintainability effort (HME) are less as compared to the low maintainability effort (LME) classes, the situation leads to imbalanced datasets for training the SMP models. The imbalanced class distribution in SMP datasets could be a dilemma for various ML techniques because, in the case of an imbalanced dataset, minority class instances are either misclassified by the ML techniques or get discarded as noise. The recent development in predictive modeling has ascertained that ensemble techniques can boost the performance of ML techniques by collating their predictions. Ensembles themselves do not solve the class-imbalance problem much. However, aggregation of ensemble techniques with the certain techniques to handle class-imbalance problem (e.g., data resampling) has led to several proposals in research. This paper evaluates the performance of ensembles for the class-imbalance in the domain of SMP. The ensembles for class-imbalance problem (ECIP) are the modification of ensembles which pre-process the imbalanced data using data resampling before the learning process. This study experimentally compares the performance of several ECIP using performance metrics Balance and g-Mean over eight Apache software datasets. The results of the study advocate that for imbalanced datasets, ECIP improves the performance of SMP models as compared to classic ensembles.

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.

Improving Logging Prediction on Imbalanced Datasets

2016 ◽  
Vol 7 (2) ◽  
pp. 43-71 ◽  
Author(s):  
Sangeeta Lal ◽  
Neetu Sardana ◽  
Ashish Sureka
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Class Imbalance ◽  
Learning Model ◽  
Learning Models ◽  
Class Imbalance Problem ◽  
Imbalanced Datasets ◽  
Imbalance Problem ◽  
Machine Learning Model ◽  
Machine Learning Models

Logging is an important yet tough decision for OSS developers. Machine-learning models are useful in improving several steps of OSS development, including logging. Several recent studies propose machine-learning models to predict logged code construct. The prediction performances of these models are limited due to the class-imbalance problem since the number of logged code constructs is small as compared to non-logged code constructs. No previous study analyzes the class-imbalance problem for logged code construct prediction. The authors first analyze the performances of J48, RF, and SVM classifiers for catch-blocks and if-blocks logged code constructs prediction on imbalanced datasets. Second, the authors propose LogIm, an ensemble and threshold-based machine-learning model. Third, the authors evaluate the performance of LogIm on three open-source projects. On average, LogIm model improves the performance of baseline classifiers, J48, RF, and SVM, by 7.38%, 9.24%, and 4.6% for catch-blocks, and 12.11%, 14.95%, and 19.13% for if-blocks logging prediction.

scholarly journals Inter-node Hellinger Distance based Decision Tree

2019 ◽  
Author(s):  
Pritom Saha Akash ◽  
Md. Eusha Kadir ◽  
Amin Ahsan Ali ◽  
Mohammad Shoyaib
Keyword(s):  
Decision Trees ◽  
State Of The Art ◽  
Class Imbalance ◽  
Hellinger Distance ◽  
Child Node ◽  
Class Imbalance Problem ◽  
Imbalanced Datasets ◽  
Weighted Version ◽  
Imbalance Problem ◽  
Splitting Criterion

This paper introduces a new splitting criterion called Inter-node Hellinger Distance (iHD) and a weighted version of it (iHDw) for constructing decision trees. iHD measures the distance between the parent and each of the child nodes in a split using Hellinger distance. We prove that this ensures the mutual exclusiveness between the child nodes. The weight term in iHDw is concerned with the purity of individual child node considering the class imbalance problem. The combination of the distance and weight term in iHDw thus favors a partition where child nodes are purer and mutually exclusive, and skew insensitive. We perform an experiment over twenty balanced and twenty imbalanced datasets. The results show that decision trees based on iHD win against six other state-of-the-art methods on at least 14 balanced and 10 imbalanced datasets. We also observe that adding the weight to iHD improves the performance of decision trees on imbalanced datasets. Moreover, according to the result of the Friedman test, this improvement is statistically significant compared to other methods.

HCBST: An Efficient Hybrid Sampling Technique for Class Imbalance Problems

2022 ◽  
Vol 16 (3) ◽  
pp. 1-37
Author(s):  
Robert A. Sowah ◽  
Bernard Kuditchar ◽  
Godfrey A. Mills ◽  
Amevi Acakpovi ◽  
Raphael A. Twum ◽  
...  
Keyword(s):  
Geometric Mean ◽  
Class Imbalance ◽  
Sampling Technique ◽  
Data Repository ◽  
Support Vector ◽  
Classification Algorithms ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
High Degree ◽  
Hybrid Sampling

Class imbalance problem is prevalent in many real-world domains. It has become an active area of research. In binary classification problems, imbalance learning refers to learning from a dataset with a high degree of skewness to the negative class. This phenomenon causes classification algorithms to perform woefully when predicting positive classes with new examples. Data resampling, which involves manipulating the training data before applying standard classification techniques, is among the most commonly used techniques to deal with the class imbalance problem. This article presents a new hybrid sampling technique that improves the overall performance of classification algorithms for solving the class imbalance problem significantly. The proposed method called the Hybrid Cluster-Based Undersampling Technique (HCBST) uses a combination of the cluster undersampling technique to under-sample the majority instances and an oversampling technique derived from Sigma Nearest Oversampling based on Convex Combination, to oversample the minority instances to solve the class imbalance problem with a high degree of accuracy and reliability. The performance of the proposed algorithm was tested using 11 datasets from the National Aeronautics and Space Administration Metric Data Program data repository and University of California Irvine Machine Learning data repository with varying degrees of imbalance. Results were compared with classification algorithms such as the K-nearest neighbours, support vector machines, decision tree, random forest, neural network, AdaBoost, naïve Bayes, and quadratic discriminant analysis. Tests results revealed that for the same datasets, the HCBST performed better with average performances of 0.73, 0.67, and 0.35 in terms of performance measures of area under curve, geometric mean, and Matthews Correlation Coefficient, respectively, across all the classifiers used for this study. The HCBST has the potential of improving the performance of the class imbalance problem, which by extension, will improve on the various applications that rely on the concept for a solution.

scholarly journals Combining Hybrid Approach Redefinition-Multiclass Imbalance (HAR-MI) and Hybrid Sampling in Handling Multi-Class Imbalance and Overlapping

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Hartono Hartono ◽  
Erianto Ongko
Keyword(s):  
Sampling Method ◽  
Hybrid Approach ◽  
Class Imbalance ◽  
Classifier Ensembles ◽  
Class Imbalance Problem ◽  
Minority Class ◽  
Imbalance Problem ◽  
Classifier Performance ◽  
R Value ◽  
Hybrid Sampling

Class imbalance is one of the main problems in classification because the number of samples in majority class is far more than the number of samples in minority class.  The class imbalance problem in the multi-class dataset is much more difficult to handle than the problem in the two class dataset. This multi-class imbalance problem is even more complicated if it is accompanied by overlapping. One method that has proven reliable in dealing with this problem is the Hybrid Approach Redefinition-Multiclass Imbalance (HAR-MI) method which is classified as a hybrid approach which combines sampling and classifier ensembles. However, in terms of diversity among classifiers, hybrid approach that combine sampling and classifier ensembles will give better results. HAR-MI delivers excellent results in handling multi-class imbalances. The HAR-MI method uses SMOTE to increase the number of sample in minority class. However, this SMOTE also has a weakness where if there is an extremely imbalanced dataset and a large number of attributes there will be over-fitting. To overcome the problem of over-fitting, the Hybrid Sampling method was proposed. HAR-MI combination with Hybrid Sampling is done to increase the number of samples in the minority class and at the same time reduce the number of noise samples in the majority class. The preprocessing stages at HAR-MI will use the Minimizing Overlapping Selection under Hybrid Sazmpling (MOSHS) method and the processing stages will use Different Contribution Sampling. The results obtained will be compared with the results using Neighbourhood-based undersampling. Overlapping and Classifier Performance will be measured using Augmented R-Value, the Matthews Correlation Coefficient (MCC), Precision, Recall, and F-Value. The results showed that HAR-MI with Hybrid Sampling gave better results in terms of Augmented R-Value, Precision, Recall, and F-Value.

scholarly journals A Comprehensive Analysis of Handling Imbalanced Dataset

2021 ◽  
Vol 10 (2) ◽  
pp. 454-463
Keyword(s):  
Machine Learning ◽  
Class Imbalance ◽  
Evaluation Process ◽  
Data Sampling ◽  
Class Imbalance Problem ◽  
Minority Class ◽  
Class Distribution ◽  
Imbalance Problem ◽  
Conventional Manner ◽  
Imbalance Dataset

Classification is a major obstacle in Machine Learning generally and also specific when tackling class imbalance problem. A dataset is said to be imbalanced if a class we are interested in falls to the minority class and appears scanty when compared to the majority class, the minority class is also known as the positive class while the majority class is also known as the negative class. Class imbalance has been a major bottleneck for Machine Learning scientist as it often leads to using wrong model for different purposes, this Survey will lead researchers to choose the right model and the best strategies to handle imbalance dataset in the course of tackling machine learning problems. Proper handling of class imbalance dataset could leads to accurate and good result. Handling class imbalance data in a conventional manner, especially when the level of imbalance is high may leads to accuracy paradox (an assumption of realizing 99% accuracy during evaluation process when the class distribution is highly imbalanced), hence imbalance class distribution requires special consideration, and for this purpose we dealt extensively on handling and solving imbalanced class problem in machine learning, such as Data Sampling Approach, Cost sensitive learning approach and Ensemble Approach.

scholarly journals RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine

2020 ◽  
Vol 12 (21) ◽  
pp. 3484 ◽  
Author(s):  
Amin Naboureh ◽  
Hamid Ebrahimy ◽  
Mohsen Azadbakht ◽  
Jinhu Bian ◽  
Meisam Amani
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Geometric Mean ◽  
Class Imbalance ◽  
Google Earth ◽  
Support Vector ◽  
Class Imbalance Problem ◽  
Imbalance Problem ◽  
Percentage Points ◽  
Under Sampling

Timely and accurate Land Cover (LC) information is required for various applications, such as climate change analysis and sustainable development. Although machine learning algorithms are most likely successful in LC mapping tasks, the class imbalance problem is known as a common challenge in this regard. This problem occurs during the training phase and reduces classification accuracy for infrequent and rare LC classes. To address this issue, this study proposes a new method by integrating random under-sampling of majority classes and an ensemble of Support Vector Machines, namely Random Under-sampling Ensemble of Support Vector Machines (RUESVMs). The performance of RUESVMs for LC classification was evaluated in Google Earth Engine (GEE) over two different case studies using Sentinel-2 time-series data and five well-known spectral indices, including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI). The performance of RUESVMs was also compared with the traditional SVM and combination of SVM with three benchmark data balancing techniques namely the Random Over-Sampling (ROS), Random Under-Sampling (RUS), and Synthetic Minority Over-sampling Technique (SMOTE). It was observed that the proposed method considerably improved the accuracy of LC classification, especially for the minority classes. After adopting RUESVMs, the overall accuracy of the generated LC map increased by approximately 4.95 percentage points, and this amount for the geometric mean of producer’s accuracies was almost 3.75 percentage points, in comparison to the most accurate data balancing method (i.e., SVM-SMOTE). Regarding the geometric mean of users’ accuracies, RUESVMs also outperformed the SVM-SMOTE method with an average increase of 6.45 percentage points.

Artificial Immune System-Based Classification in Extremely Imbalanced Classification Problems

2017 ◽  
Vol 26 (03) ◽  
pp. 1750009 ◽  
Author(s):  
Dionisios N. Sotiropoulos ◽  
George A. Tsihrintzis
Keyword(s):  
Machine Learning ◽  
Immune System ◽  
Artificial Immune System ◽  
Class Imbalance ◽  
Classification Algorithm ◽  
Support Vector ◽  
Artificial Immune ◽  
Class Imbalance Problem ◽  
Minority Class ◽  
Imbalance Problem

This paper focuses on a special category of machine learning problems arising in cases where the set of available training instances is significantly biased towards a particular class of patterns. Our work addresses the so-called Class Imbalance Problem through the utilization of an Artificial Immune System-(AIS)based classification algorithm which encodes the inherent ability of the Adaptive Immune System to mediate the exceptionally imbalanced “self” / “non-self” discrimination process. From a computational point of view, this process constitutes an extremely imbalanced pattern classification task since the vast majority of molecular patterns pertain to the “non-self” space. Our work focuses on investigating the effect of the class imbalance problem on the AIS-based classification algorithm by assessing its relative ability to deal with extremely skewed datasets when compared against two state-of-the-art machine learning paradigms such as Support Vector Machines (SVMs) and Multi-Layer Perceptrons (MLPs). To this end, we conducted a series of experiments on a music-related dataset where a small fraction of positive samples was to be recognized against the vast volume of negative samples. The results obtained indicate that the utilized bio-inspired classifier outperforms SVMs in detecting patterns from the minority class while its performance on the same task is competently close to the one exhibited by MLPs. Our findings suggest that the AIS-based classifier relies on its intrinsic resampling and class-balancing functionality in order to address the class imbalance problem.

scholarly journals A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems

2021 ◽  
Vol 11 (14) ◽  
pp. 6310
Author(s):  
Ismael Lin ◽  
Octavio Loyola-González ◽  
Raúl Monroy ◽  
Miguel Angel Medina-Pérez
Keyword(s):  
Pattern Recognition ◽  
Real World ◽  
State Of The Art ◽  
Class Imbalance ◽  
Class Imbalance Problem ◽  
Real World Data ◽  
Minority Class ◽  
Research Directions ◽  
Imbalance Problem ◽  
Medical Diagnostic

The usage of imbalanced databases is a recurrent problem in real-world data such as medical diagnostic, fraud detection, and pattern recognition. Nevertheless, in class imbalance problems, the classifiers are commonly biased by the class with more objects (majority class) and ignore the class with fewer objects (minority class). There are different ways to solve the class imbalance problem, and there has been a trend towards the usage of patterns and fuzzy approaches due to the favorable results. In this paper, we provide an in-depth review of popular methods for imbalanced databases related to patterns and fuzzy approaches. The reviewed papers include classifiers, data preprocessing, and evaluation metrics. We identify different application domains and describe how the methods are used. Finally, we suggest further research directions according to the analysis of the reviewed papers and the trend of the state of the art.

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