Comparing the performance of oversampling techniques in combination with a clustering algorithm for imbalanced learning

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceImbalanced datasets in supervised learning are considered an ongoing challenging task for standard algorithms, seeing as they are designed to handle balanced class distributions and perform poorly when applied to problems of the imbalanced nature. Many methods have been developed to address this specific problem but the more general approach to achieve a balanced class distribution is data level modification, instead of algorithm modifications. Although class imbalances are responsible for significant losses of performance in standard classifiers in many different types of problems, another aspect that is important to consider is the small disjuncts problem. Therefore, it is important to consider and understand solutions that not only take into the account the between-class imbalance (the imbalance occurring between the two classes) but also the within-class imbalance (the imbalance occurring between the sub-clusters of each class) and to oversample the dataset by rectifying these two types of imbalances simultaneously. It has been shown that cluster-based oversampling is a robust solution that takes into consideration these two problems. This work sets out to study the effect and impact combining different existing oversampling methods with a clustering-based approach. Empirical results of extensive experiments show that the combinations of different oversampling techniques with the clustering algorithm k-means – K-Means Oversampling - improves upon classification results resulting solely from the oversampling techniques with no prior clustering step

Bridging the Gap: Simultaneous Fine Tuning for Data Re-Balancing

There are many real-world classification problems wherein the issue of data imbalance (the case when a data set contains substantially more samples for one/many classes than the rest) is unavoidable. While under-sampling the problematic classes is a common solution, this is not a compelling option when the large data class is itself diverse and/or the limited data class is especially small. We suggest a strategy based on recent work concerning limited data problems which utilizes a supplemental set of images with similar properties to the limited data class to aid in the training of a neural network. We show results for our model against other typical methods on a real-world synthetic aperture sonar data set. Code can be found at github.com/JohnMcKay/dataImbalance.Comment: Submitted to IGARSS 2018, 4 Pages, 8 Figure

Adaptive subspace sampling for class imbalance processing

© 2016 IEEE. This paper presents a novel oversampling technique that addresses highly imbalanced data distribution. At present, the imbalanced data that have anomalous class distribution and underrepresented data are difficult to deal with through a variety of conventional machine learning technologies. In order to balance class distributions, an adaptive subspace self-organizing map (ASSOM) that combines the local mapping scheme and globally competitive rule is proposed to artificially generate synthetic samples focusing on minority class samples. The ASSOM is conformed with feature-invariant characteristics, including translation, scaling and rotation, and it retains the independence of basis vectors in each module. Specifically, basis vectors generated via each ASSOM module can avoid generating repeated representative features that offer nothing but heavy computational load. Several experimental results demonstrate that the proposed ASSOM method with supervised learning manner is superior to other existing oversampling techniques

Fuzzy distance-based undersampling technique for imbalanced flood data

Performances of classifiers are affected by imbalanced data because instances in the minority class are often ignored. Imbalanced data often occur in many application domains including flood. If flood cases are misclassified, the impact of flood is higher than the misclassification of non-flood cases.Numerous resampling techniques such as undersampling and oversampling have been used to overcome the problem of misclassification of imbalanced data.However, the undersampling and oversampling techniques suffer from elimination of relevant data and overfitting, which may lead to poor classification results.This paper proposes a Fuzzy Distance-based Undersampling (FDUS) technique to increase classification accuracy. Entropy estimation is used to generate fuzzy thresholds which are used to categorise the instances in majority and minority classes into membership functions. The performance of FDUS was compared with three techniques based on Fmeasure and G-mean, experimented on flood data. From the results, FDUS achieved better F-measure and G-mean compared to the other techniques which showed that the FDUS was able to reduce the elimination of relevant data

ESSAY ANSWER CLASSIFICATION WITH SMOTE RANDOM FOREST AND ADABOOST IN AUTOMATED ESSAY SCORING

 Automated essay scoring (AES) is used to evaluate and assessment student essays are written based on the questions given. However, there are difficulties in conducting automatic assessments carried out by the system, these difficulties occur due to typing errors (typos), the use of regional languages , or incorrect punctuation. These errors make the assessment less consistent and accurate. Based on the dataset analysis that has been carried out, there is an imbalance between the number of right and wrong answers, so a technique is needed to overcome the data imbalance. Based on the literature, to overcome these problems, the Random Forest and AdaBoost classification algorithms can be used to improve the consistency of classification accuracy and the SMOTE method to overcome data imbalances.The Random Forest method using SMOTE can achieve an F1 measure of 99%, which means that the hybrid method can overcome the problem of imbalanced datasets that are limited to AES. The AdaBoost model with SMOTE produces the highest F1 measure reaching 99% of the entire dataset. The structure of the dataset is something that also affects the performance of the model. So the best model obtained in this study is the Random Forest model with SMOTE

An enhanced resampling technique for imbalanced data sets

A data set is considered imbalanced if the distribution of instances in one class (majority class) outnumbers the other class (minority class). The main problem related to binary imbalanced data sets is classifiers tend to ignore the minority class. Numerous resampling techniques such as undersampling, oversampling, and a combination of both techniques have been widely used. However, the undersampling and oversampling techniques suffer from elimination and addition of relevant data which may lead to poor classification results. Hence, this study aims to increase classification metrics by enhancing the undersampling technique and combining it with an existing oversampling technique. To achieve this objective, a Fuzzy Distancebased Undersampling (FDUS) is proposed. Entropy estimation is used to produce fuzzy thresholds to categorise the instances in majority and minority class into membership functions. FDUS is then combined with the Synthetic Minority Oversampling TEchnique (SMOTE) known as FDUS+SMOTE, which is executed in sequence until a balanced data set is achieved. FDUS and FDUS+SMOTE are compared with four techniques based on classification accuracy, F-measure and Gmean. From the results, FDUS achieved better classification accuracy, F-measure and G-mean, compared to the other techniques with an average of 80.57%, 0.85 and 0.78, respectively. This showed that fuzzy logic when incorporated with Distance-based Undersampling technique was able to reduce the elimination of relevant data. Further, the findings showed that FDUS+SMOTE performed better than combination of SMOTE and Tomek Links, and SMOTE and Edited Nearest Neighbour on benchmark data sets. FDUS+SMOTE has minimised the removal of relevant data from the majority class and avoid overfitting. On average, FDUS and FDUS+SMOTE were able to balance categorical, integer and real data sets and enhanced the performance of binary classification. Furthermore, the techniques performed well on small record size data sets that have of instances in the range of approximately 100 to 800