Imbalance Learning and Its Application on Medical Datasets

To gain more valuable information from the increasing large amount of data, data mining has been a hot topic that attracts growing attention in this two decades. One of the challenges in data mining is imbalance learning, which refers to leaning from imbalanced datasets. The imbalanced datasets is dominated by some classes (majority) and other under-represented classes (minority). The imbalanced datasets degrade the learning ability of traditional methods, which are designed on the assumption that all classes are balanced and have equal misclassification costs, leading to the poor performance on the minority classes. This phenomenon is usually called the class imbalance problem. However, it is usually the minority classes of more interest and importance, such as sick cases in the medical dataset. Additionally, traditional methods are optimized to achieve maximum accuracy, which is not suitable for evaluating the performance on imbalanced datasets. From the view of data space, class imbalance could be classified as extrinsic imbalance and intrinsic imbalance. Extrinsic imbalance is caused by external factors, such as data transmission or data storage, while intrinsic imbalance means the dataset is inherently imbalanced due to its nature.  As extrinsic imbalance could be fixed by collecting more samples, this thesis mainly focus on on two scenarios of the intrinsic imbalance,  machine learning for imbalanced structured datasets and deep learning for imbalanced image datasets.  Normally, the solutions for the class imbalance problem are named as imbalance learning methods, which could be grouped into data-level methods (re-sampling), algorithm-level (re-weighting) methods and hybrid methods. Data-level methods modify the class distribution of the training dataset to create balanced training sets, and typical examples are over-sampling and under-sampling. Instead of modifying the data distribution, algorithm-level methods adjust the misclassification cost to alleviate the class imbalance problem, and one typical example is cost sensitive methods. Hybrid methods usually combine data-level methods and algorithm-level methods. However, existing imbalance learning methods encounter different kinds of problems. Over-sampling methods increase the minority samples to create balanced training sets, which might lead the trained model overfit to the minority class. Under-sampling methods create balanced training sets by discarding majority samples, which lead to the information loss and poor performance of the trained model. Cost-sensitive methods usually need assistance from domain expert to define the misclassification costs which are task specified. Thus, the generalization ability of cost-sensitive methods is poor. Especially, when it comes to the deep learning methods under class imbalance, re-sampling methods may introduce large computation cost and existing re-weighting methods could lead to poor performance. The object of this dissertation is to understand features difference under class imbalance, to improve the classification performance on structured datasets or image datasets. This thesis proposes two machine learning methods for imbalanced structured datasets and one deep learning method for imbalance image datasets. The proposed methods are evaluated on several medical datasets, which are intrinsically imbalanced.  Firstly, we study the feature difference between the majority class and the minority class of an imbalanced medical dataset, which is collected from a Chinese hospital. After data cleaning and structuring, we get 3292 kidney stone cases treated by Percutaneous Nephrolithonomy from 2012 to 2019. There are 651 (19.78% ) cases who have postoperative complications, which makes the complication prediction an imbalanced classification task. We propose a sampling-based method SMOTE-XGBoost and implement it to build a postoperative complication prediction model. Experimental results show that the proposed method outperforms classic machine learning methods. Furthermore, traditional prediction models of Percutaneous Nephrolithonomy are designed to predict the kidney stone status and overlook complication related features, which could degrade their prediction performance on complication prediction tasks. To this end, we merge more features into the proposed sampling-based method and further improve the classification performance. Overall, SMOTE-XGBoost achieves an AUC of 0.7077 which is 41.54% higher than that of S.T.O.N.E. nephrolithometry, a traditional prediction model of Percutaneous Nephrolithonomy. After reviewing the existing machine learning methods under class imbalance, we propose a novel ensemble learning approach called Multiple bAlance Subset Stacking (MASS). MASS first cuts the majority class into multiple subsets by the size of the minority set, and combines each majority subset with the minority set as one balanced subsets. In this way, MASS could overcome the problem of information loss because it does not discard any majority sample. Each balanced subset is used to train one base classifier. Then, the original dataset is feed to all the trained base classifiers, whose output are used to generate the stacking dataset. One stack model is trained by the staking dataset to get the optimal weights for the base classifiers. As the stacking dataset keeps the same labels as the original dataset, which could avoid the overfitting problem. Finally, we can get an ensembled strong model based on the trained base classifiers and the staking model. Extensive experimental results on three medical datasets show that MASS outperforms baseline methods.  The robustness of MASS is proved over implementing different base classifiers. We design a parallel version MASS to reduce the training time cost. The speedup analysis proves that Parallel MASS could reduce training time cost greatly when applied on large datasets. Specially, Parallel MASS reduces 101.8% training time compared with MASS at most in our experiments.  When it comes to the class imbalance problem of image datasets, existing imbalance learning methods suffer from the problem of large training cost and poor performance.  After introducing the problem of implementing resampling methods on image classification tasks, we demonstrate issues of re-weighting strategy using class frequencies through the experimental result on one medical image dataset.  We propose a novel re-weighting method Hardness Aware Dynamic loss to solve the class imbalance problem of image datasets. After each training epoch of deep neural networks, we compute the classification hardness of each class. We will assign higher class weights to the classes have large classification hardness values and vice versa in the next epoch. In this way, HAD could tune the weight of each sample in the loss function dynamically during the training process. The experimental results prove that HAD significantly outperforms the state-of-the-art methods. Moreover, HAD greatly improves the classification accuracies of minority classes while only making a small compromise of majority class accuracies. Especially, HAD loss improves 10.04% average precision compared with the best baseline, Focal loss, on the HAM10000 dataset. At last, I conclude this dissertation with our contributions to the imbalance learning, and provide an overview of potential directions for future research, which include extensions of the three proposed methods, development of task-specified algorithms, and fixing the challenges of within-class imbalance.2021-06-0

Coupling different methods for overcoming the class imbalance problem

Many classification problems must deal with imbalanced datasets where one class \u2013 the majority class \u2013 outnumbers the other classes. Standard classification methods do not provide accurate predictions in this setting since classification is generally biased towards the majority class. The minority classes are oftentimes the ones of interest (e.g., when they are associated with pathological conditions in patients), so methods for handling imbalanced datasets are critical. Using several different datasets, this paper evaluates the performance of state-of-the-art classification methods for handling the imbalance problem in both binary and multi-class datasets. Different strategies are considered, including the one-class and dimension reduction approaches, as well as their fusions. Moreover, some ensembles of classifiers are tested, in addition to stand-alone classifiers, to assess the effectiveness of ensembles in the presence of imbalance. Finally, a novel ensemble of ensembles is designed specifically to tackle the problem of class imbalance: the proposed ensemble does not need to be tuned separately for each dataset and outperforms all the other tested approaches. To validate our classifiers we resort to the KEEL-dataset repository, whose data partitions (training/test) are publicly available and have already been used in the open literature: as a consequence, it is possible to report a fair comparison among different approaches in the literature. Our best approach (MATLAB code and datasets not easily accessible elsewhere) will be available at https://www.dei.unipd.it/node/2357

A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation

Class imbalance (CI) in classification problems arises when the number of observations belonging to one class is lower than the other. Ensemble learning combines multiple models to obtain a robust model and has been prominently used with data augmentation methods to address class imbalance problems. In the last decade, a number of strategies have been added to enhance ensemble learning and data augmentation methods, along with new methods such as generative adversarial networks (GANs). A combination of these has been applied in many studies, and the evaluation of different combinations would enable a better understanding and guidance for different application domains. In this paper, we present a computational study to evaluate data augmentation and ensemble learning methods used to address prominent benchmark CI problems. We present a general framework that evaluates 9 data augmentation and 9 ensemble learning methods for CI problems. Our objective is to identify the most effective combination for improving classification performance on imbalanced datasets. The results indicate that combinations of data augmentation methods with ensemble learning can significantly improve classification performance on imbalanced datasets. We find that traditional data augmentation methods such as the synthetic minority oversampling technique (SMOTE) and random oversampling (ROS) are not only better in performance for selected CI problems, but also computationally less expensive than GANs. Our study is vital for the development of novel models for handling imbalanced datasets

Predictive Framework for Imbalance Dataset

The purpose of this research is to seek and propose a new predictive maintenance framework which can be used to generate a prediction model for deterioration of process materials. Real yield data which was obtained from Fuji Electric Malaysia has been used in this research. The existing data pre-processing and classification methodologies have been adapted in this research. Properties of the proposed framework include; developing an approach to correlate materials defects, developing an approach to represent data attributes features, analyzing various ratio and types of data re-sampling, analyzing the impact of data dimension reduction for various data size, and partitioning data size and algorithmic schemes against the prediction performance. Experimental results suggested that the class probability distribution function of a prediction model has to be closer to a training dataset; less skewed environment enable learning schemes to discover better function F in a bigger Fall space within a higher dimensional feature space, data sampling and partition size is appear to proportionally improve the precision and recall if class distribution ratios are balanced. A comparative study was also conducted and showed that the proposed approaches have performed better. This research was conducted based on limited number of datasets, test sets and variables. Thus, the obtained results are applicable only to the study domain with selected datasets. This research has introduced a new predictive maintenance framework which can be used in manufacturing industries to generate a prediction model based on the deterioration of process materials. Consequently, this may allow manufactures to conduct predictive maintenance not only for equipments but also process materials. The major contribution of this research is a step by step guideline which consists of methods/approaches in generating a prediction for process materials

Stacked Generalizations in Imbalanced Fraud Data Sets using Resampling Methods

This study uses stacked generalization, which is a two-step process of combining machine learning methods, called meta or super learners, for improving the performance of algorithms in step one (by minimizing the error rate of each individual algorithm to reduce its bias in the learning set) and then in step two inputting the results into the meta learner with its stacked blended output (demonstrating improved performance with the weakest algorithms learning better). The method is essentially an enhanced cross-validation strategy. Although the process uses great computational resources, the resulting performance metrics on resampled fraud data show that increased system cost can be justified. A fundamental key to fraud data is that it is inherently not systematic and, as of yet, the optimal resampling methodology has not been identified. Building a test harness that accounts for all permutations of algorithm sample set pairs demonstrates that the complex, intrinsic data structures are all thoroughly tested. Using a comparative analysis on fraud data that applies stacked generalizations provides useful insight needed to find the optimal mathematical formula to be used for imbalanced fraud data sets.Comment: 19 pages, 3 figures, 8 table

Credit scoring: comparison of non‐parametric techniques against logistic regression

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceOver the past decades, financial institutions have been giving increased importance to credit risk management as a critical tool to control their profitability. More than ever, it became crucial for these institutions to be able to well discriminate between good and bad clients for only accepting the credit applications that are not likely to default. To calculate the probability of default of a particular client, most financial institutions have credit scoring models based on parametric techniques. Logistic regression is the current industry standard technique in credit scoring models, and it is one of the techniques under study in this dissertation. Although it is regarded as a robust and intuitive technique, it is still not free from several critics towards the model assumptions it takes that can compromise its predictions. This dissertation intends to evaluate the gains in performance resulting from using more modern non-parametric techniques instead of logistic regression, performing a model comparison over four different real-life credit datasets. Specifically, the techniques compared against logistic regression in this study consist of two single classifiers (decision tree and SVM with RBF kernel) and two ensemble methods (random forest and stacking with cross-validation). The literature review demonstrates that heterogeneous ensemble approaches have a weaker presence in credit scoring studies and, because of that, stacking with cross-validation was considered in this study. The results demonstrate that logistic regression outperforms the decision tree classifier, has similar performance in relation to SVM and slightly underperforms both ensemble approaches in similar extents

Online Tool Condition Monitoring Based on Parsimonious Ensemble+

Accurate diagnosis of tool wear in metal turning process remains an open challenge for both scientists and industrial practitioners because of inhomogeneities in workpiece material, nonstationary machining settings to suit production requirements, and nonlinear relations between measured variables and tool wear. Common methodologies for tool condition monitoring still rely on batch approaches which cannot cope with a fast sampling rate of metal cutting process. Furthermore they require a retraining process to be completed from scratch when dealing with a new set of machining parameters. This paper presents an online tool condition monitoring approach based on Parsimonious Ensemble+, pENsemble+. The unique feature of pENsemble+ lies in its highly flexible principle where both ensemble structure and base-classifier structure can automatically grow and shrink on the fly based on the characteristics of data streams. Moreover, the online feature selection scenario is integrated to actively sample relevant input attributes. The paper presents advancement of a newly developed ensemble learning algorithm, pENsemble+, where online active learning scenario is incorporated to reduce operator labelling effort. The ensemble merging scenario is proposed which allows reduction of ensemble complexity while retaining its diversity. Experimental studies utilising real-world manufacturing data streams and comparisons with well known algorithms were carried out. Furthermore, the efficacy of pENsemble was examined using benchmark concept drift data streams. It has been found that pENsemble+ incurs low structural complexity and results in a significant reduction of operator labelling effort.Comment: this paper has been published by IEEE Transactions on Cybernetic

Intrusion detection by machine learning = Behatolás detektálás gépi tanulás által

Since the early days of information technology, there have been many stakeholders who used the technological capabilities for their own benefit, be it legal operations, or illegal access to computational assets and sensitive information. Every year, businesses invest large amounts of effort into upgrading their IT infrastructure, yet, even today, they are unprepared to protect their most valuable assets: data and knowledge. This lack of protection was the main reason for the creation of this dissertation. During this study, intrusion detection, a field of information security, is evaluated through the use of several machine learning models performing signature and hybrid detection. This is a challenging field, mainly due to the high velocity and imbalanced nature of network traffic. To construct machine learning models capable of intrusion detection, the applied methodologies were the CRISP-DM process model designed to help data scientists with the planning, creation and integration of machine learning models into a business information infrastructure, and design science research interested in answering research questions with information technology artefacts. The two methodologies have a lot in common, which is further elaborated in the study. The goals of this dissertation were two-fold: first, to create an intrusion detector that could provide a high level of intrusion detection performance measured using accuracy and recall and second, to identify potential techniques that can increase intrusion detection performance. Out of the designed models, a hybrid autoencoder + stacking neural network model managed to achieve detection performance comparable to the best models that appeared in the related literature, with good detections on minority classes. To achieve this result, the techniques identified were synthetic sampling, advanced hyperparameter optimization, model ensembles and autoencoder networks. In addition, the dissertation set up a soft hierarchy among the different detection techniques in terms of performance and provides a brief outlook on potential future practical applications of network intrusion detection models as well