Learning from Imbalanced Multi-label Data Sets by Using Ensemble Strategies

Multi-label classification is an extension of conventional classification in which a single instance can be associated with multiple labels. Problems of this type are ubiquitous in everyday life. Such as, a movie can be categorized as action, crime, and thriller. Most algorithms on multi-label classification learning are designed for balanced data and don’t work well on imbalanced data. On the other hand, in real applications, most datasets are imbalanced. Therefore, we focused to improve multi-label classification performance on imbalanced datasets. In this paper, a state-of-the-art multi-label classification algorithm, which called IBLR_ML, is employed. This algorithm is produced from combination of k-nearest neighbor and logistic regression algorithms. Logistic regression part of this algorithm is combined with two ensemble learning algorithms, Bagging and Boosting. My approach is called IB-ELR. In this paper, for the first time, the ensemble bagging method whit stable learning as the base learner and imbalanced data sets as the training data is examined. Finally, to evaluate the proposed methods; they are implemented in JAVA language. Experimental results show the effectiveness of proposed methods. Keywords: Multi-label classification, Imbalanced data set, Ensemble learning, Stable algorithm, Logistic regression, Bagging, Boostin

HAR-MI method for multi-class imbalanced datasets

Research on multi-class imbalance from a number of researchers faces obstacles in the form of poor data diversity and a large number of classifiers. The Hybrid Approach Redefinition-Multiclass Imbalance (HAR-MI) method is a Hybrid Ensembles method which is the development of the Hybrid Approach Redefinion (HAR) method. This study has compared the results obtained with the Dynamic Ensemble Selection-Multiclass Imbalance (DES-MI) method in handling multiclass imbalance. In the HAR-MI Method, the preprocessing stage was carried out using the random balance ensembles method and dynamic ensemble selection to produce a candidate ensemble and the processing stages was carried out using different contribution sampling and dynamic ensemble selection to produce a candidate ensemble. This research has been conducted by using multi-class imbalance datasets sourced from the KEEL Repository. The results show that the HAR-MI method can overcome multi-class imbalance with better data diversity, smaller number of classifiers, and better classifier performance compared to a DES-MI method. These results were tested with a Wilcoxon signed-rank statistical test which showed that the superiority of the HAR-MI method with respect to DES-MI method

Extracting Features from Textual Data in Class Imbalance Problems

[EN] We address class imbalance problems. These are classification problems where the target variable is binary, and one class dominates over the other. A central objective in these problems is to identify features that yield models with high precision/recall values, the standard yardsticks for assessing such models. Our features are extracted from the textual data inherent in such problems. We use n-gram frequencies as features and introduce a discrepancy score that measures the efficacy of an n-gram in highlighting the minority class. The frequency counts of n-grams with the highest discrepancy scores are used as features to construct models with the desired metrics. According to the best practices followed by the services industry, many customer support tickets will get audited and tagged as contract-compliant whereas some will be tagged as over-delivered . Based on in-field data, we use a random forest classifier and perform a randomized grid search over the model hyperparameters. The model scoring is performed using an scoring function. Our objective is to minimize the follow-up costs by optimizing the recall score while maintaining a base-level precision score. The final optimized model achieves an acceptable recall score while staying above the target precision. We validate our feature selection method by comparing our model with one constructed using frequency counts of n-grams chosen randomly. We propose extensions of our feature extraction method to general classification (binary and multi-class) and regression problems. The discrepancy score is one measure of dissimilarity of distributions and other (more general) measures that we formulate could potentially yield more effective models.Aravamuthan, S.; Jogalekar, P.; Lee, J. (2022). Extracting Features from Textual Data in Class Imbalance Problems. Journal of Computer-Assisted Linguistic Research. 6:42-58. https://doi.org/10.4995/jclr.2022.182004258

Comparison of Sampling Methods for Predicting Wine Quality Based on Physicochemical Properties

Using the physicochemical properties of wine to predict quality has been done in numerous studies. Given the nature of these properties, the data is inherently skewed. Previous works have focused on handful of sampling techniques to balance the data. This research compares multiple sampling techniques in predicting the target with limited data. For this purpose, an ensemble model is used to evaluate the different techniques. There was no evidence found in this research to conclude that there are specific oversampling methods that improve random forest classifier for a multi-class problem

Learning from class-imbalanced data: overlap-driven resampling for imbalanced data classification.

Classification of imbalanced datasets has attracted substantial research interest over the past years. This is because imbalanced datasets are common in several domains such as health, finance and security, but learning algorithms are generally not designed to handle them. Many existing solutions focus mainly on the class distribution problem. However, a number of reports showed that class overlap had a higher negative impact on the learning process than class imbalance. This thesis thoroughly explores the impact of class overlap on the learning algorithm and demonstrates how elimination of class overlap can effectively improve the classification of imbalanced datasets. Novel undersampling approaches were developed with the main objective of enhancing the presence of minority class instances in the overlapping region. This is achieved by identifying and removing majority class instances potentially residing in such a region. Seven methods under the two different approaches were designed for the task. Extensive experiments were carried out to evaluate the methods on simulated and well-known real-world datasets. Results showed that substantial improvement in the classification accuracy of the minority class was obtained with favourable trade-offs with the majority class accuracy. Moreover, successful application of the methods in predictive diagnostics of diseases with imbalanced records is presented. These novel overlap-based approaches have several advantages over other common resampling methods. First, the undersampling amount is independent of class imbalance and proportional to the degree of overlap. This could effectively address the problem of class overlap while reducing the effect of class imbalance. Second, information loss is minimised as instance elimination is contained within the problematic region. Third, adaptive parameters enable the methods to be generalised across different problems. It is also worth pointing out that these methods provide different trade-offs, which offer more alternatives to real-world users in selecting the best fit solution to the problem

A survey on machine learning for recurring concept drifting data streams

The problem of concept drift has gained a lot of attention in recent years. This aspect is key in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks affecting their generative processes. In this survey, we review the relevant literature to deal with regime changes in the behaviour of continuous data streams. The study starts with a general introduction to the field of data stream learning, describing recent works on passive or active mechanisms to adapt or detect concept drifts, frequent challenges in this area, and related performance metrics. Then, different supervised and non-supervised approaches such as online ensembles, meta-learning and model-based clustering that can be used to deal with seasonalities in a data stream are covered. The aim is to point out new research trends and give future research directions on the usage of machine learning techniques for data streams which can help in the event of shifts and recurrences in continuous learning scenarios in near real-time