Research on Ship Classification Based on Trajectory Association

Automation in arrhythmia classification helps medical professionals to make accurate decisions upon the patient's health. Classification becomes complicated when class overlapping and class imbalance problem occurs together. The aim of this work is to improve the arrhythmia classification accuracy. Proposed methodology consists of fisher discriminant ratio based feature ranking stage and anomaly detection based training sample selection stage followed by classification using probabilistic neural network classifier. As per the recommendations of the Association for the Advancement of Medical Instrumentation, five arrhythmia classes were classified. The proposed method resulted in average sensitivity, positive predictive value and F Score of 95.37%, 98.35% and 96.72%, respectively. The experimental results revealed that: (1) Selected non-overlapping features were able to better discriminate arrhythmia classes, (2) Mixture of Gaussians based anomaly detection method suited well to handle the class imbalance problem and (3) Minority classes with few training samples were also correctly classified using the proposed method.

The class imbalance problem is a well-known classification challenge in machine learning that has vexed researchers for over a decade. Under-representation of one or more of the target classes (minority class(es)) as compared to others (majority class(es)) can restrict the application of conventional classifiers directly on the data. In addition, emerging challenges such as overlapping classes, make class imbalance even harder to solve. Class overlap is caused due to ambiguous regions in the data where the prior probability of two or more classes are approximately equal. We are motivated to address the challenge of class overlap in the presence of imbalanced classes by a problem in pervasive computing. Specifically, we are designing smart environments that perform health monitoring and assistance. Our solution, ClusBUS, is a clustering-based under sampling technique that identifies data regions where minority class samples are embedded deep inside majority class. By removing majority class samples from these regions, ClusBUS preprocesses the data in order to give more importance to the minority class during classification. Experiments show that ClusBUS achieves improved performance over an existing method for handling class imbalance.

Textual stream classification has become a realistic and challenging issue since large-scale, high-dimensional, and non-stationary streams with class imbalance have been widely used in various real-life applications. According to the characters of textual streams, it is technically difficult to deal with the classification of textual stream, especially in imbalanced environment. In this paper, we propose a new ensemble framework, clustering forest, for learning from the textual imbalanced stream with concept drift (CFIM). The CFIM is based on ensemble learning by integrating a set of clustering trees (CTs). An adaptive selection method, which flexibly chooses the useful CTs by the property of the stream, is presented in CFIM. In particular, to deal with the problem of class imbalance, we collect and reuse both rare-class instances and misclassified instances from the historical chunks. Compared to most existing approaches, it is worth pointing out that our approach assumes that both majority class and rareclass may suffer from concept drift. Thus the distribution of resampled instances is similar to the current concept. The effectiveness of CFIM is examined in five real-world textual streams under an imbalanced nonstationary environment. Experimental results demonstrate that CFIM achieves better performance than four state-of-the-art ensemble models.

The problem of class imbalance is often encountered in remote sensing data and has a negative effect on the classification performance of supervised classifiers even in ensemble models. The ensemble margin is a fundamental concept in ensemble learning with potential effectiveness in improving the classification of remote sensing data. This paper proposes a novel margin based extended random forest algorithm to address the class imbalance issues in the difficult context of remote sensing classification. This algorithm combines ensemble learning with data sampling. A comparative analysis is conducted with respect to standard random forest, undersampling and over-sampling combined ensembles.

Approaches to imbalanced classification problem usually focus on rebalancing the class sizes, neglecting the effect of hidden structure within the majority class. The aim of this paper is to highlight the effect of sub-clusters within the majority class on detecting minority class instances, and handle imbalanced classification by learning the structure in the data. We propose a decomposition based approach to two-class imbalanced classification problem. This approach works by first learning the hidden structure of the majority class using an unsupervised learning algorithm. Thus, transforming the classification problem into several classification sub-problems. The base classifier is constructed on each sub-problem. The ensemble is tuned to increase its sensitivity towards minority class. The proposed approach overcomes the limitations of conventional classifiers on imbalanced problem, and combats imbalance by learning hidden structure in the majority class, which is neglected in most existing works. We demonstrate the performance of the proposed approach on several datasets.

Oversampling is an efficient technique in dealing with class-imbalance problem. It addresses the problem by reduplicating or generating the minority class samples to balance the distribution between the samples of the majority and the minority class. Synthetic minority oversampling technique (SMOTE) is one of the typical representatives. During the past decade, researchers have proposed many variants of SMOTE. However, the existing oversampling methods may generate wrong minority class samples in some scenarios. Furthermore, how to effectively mine the inherent complex characteristics of imbalanced data remains a challenge. To this end, this paper proposes a parameter-free data cleaning method to improve SMOTE based on constructive covering algorithm. The dataset generated by SMOTE is first partitioned into a group of covers, then the hard-to-learn samples can be detected based on the characteristics of sample space distribution. Finally, a pair-wise deletion strategy is proposed to remove the hard-to-learn samples. The experimental results on 25 imbalanced datasets show that our proposed method is superior to the comparison methods in terms of various metrics, such as F-measure, G-mean, and Recall. Our method not only can reduce the complexity of the dataset but also can improve the performance of the classification model.

MultiBoost ensemble has been well acknowledged as an effective learning algorithm which able to reduce both bias and variance in error and has high generalization performance. However, to deal with the class imbalanced learning, the Multi- Boost shall be amended. In this paper, a new hybrid machine learning method called Distribution based MultiBoost (DBMB) for class imbalanced problems is proposed, which combines Distribution based balanced sampling with the MultiBoost algo- rithm to achieve better minority class performance. It minimizes the within class and between class imbalance by learning and sampling different distributions (Gaussian and Poisson) and reduces bias and variance in error by employing the MultiBoost ensemble. Therefore, DBMB could output the final strong learner that is more proficient ensemble of weak base learners for imbalanced data sets. We prove that the G-mean, F1 measure and AUC of the DBMB is significantly superior to others. The experimental verification has shown that the proposed DBMB outperforms other state-of-the-art algorithms on many real world class imbalanced problems. Furthermore, our proposed method is scalable as compare to other boosting methods.

The problem of class imbalance in machine learning occurs when there is a relatively big disproportional distribution of classes in the data for classification tasks. In many real-world domains, such as in healthcare, finance, and predictive maintenance, the number of data points of a less important class (usually the negative class) is much higher than the class of greater interest (usually the positive or target class). This affects the ability of many learning algorithms to find good classification models. To address that, many approaches for solving this problem have been proposed, prominently including ensemble methods integrated with sampling-based techniques. However, these methods are still prone to the negative effects of sampling-based techniques that alter class distributions via over-sampling or under-sampling, which can lead to overfitting or discarding useful data, respectively, and thus affect performance. In this paper, we propose a new data preprocessing sampling technique dubbed as (sBal) for ensemble methods for binary classification in the case of imbalanced datasets. Our proposed method first turns the imbalanced dataset into several balanced bins/bags. Then multiple base learners are induced on the balanced bags and finally, the classification results are combined using a specific ensemble rule. We evaluated the performance of our proposed method on 50 imbalanced real-world binary datasets and compared its performance with well-known ensemble methods that utilize data preprocessing techniques namely SMOTEBagging, SMOTEBoost, RUSBoost, and RAMOBoost. The results reveal that the proposed method brings considerable improvement in classification performance relevant to the compared methods. We performed statistical significance analysis using Friedman’s non-parametric statistical test with Bergman post-hoc test. The analysis showed that our method performed significantly better than the majority of the methods across many datasets, suggesting a better preprocessing approach than the ones used in compared methods. We also highlight possible extensions to the method that can improve its effectiveness.KeywordsData samplingEnsemble methodsImbalanced datasetsSplit balancingRAMOBoostSMOTEBaggingSMOTEBoost

In this paper, a ship classification framework based on deep learning is proposed. We focus on the influence of the incident angle factor for the classification results on deep learning-based methods. A representative SAR ship dataset containing three types of ship and the coverage of incidence angle is approximately from 20° to 60° is created. We evaluated the training-test performance of four deep learning models on the dataset. Taking cargo ship as the example, the experimental results show that when using data with different range of incident angles for training, the classification performance on test set with different range of incident angles varies greatly. The first analysis of the incident angle factor in SAR ship classification using deep learning methods allowed researchers to select appropriate data when using the deep learning method to classify ships in SAR images, and may suggest satellite parameters based on the classification results.

The imbalance data problem in classification is a significant research area and has attracted a lot attention in recent years. Rebalancing class distribution techniques such as over-sampling or under-sampling are the most common approaches to deal with this problem. This paper presents a new method so called Diversity and Separable Metrics in Over-Sampling Technique (DSMOTE) to handle the imbalanced learning problems. The main idea of the DSMOTE is to use a diversity and separable measure which shows a positive impact on the minority class. This improvement is achieved by reduce overfitting by using a diversity measure. Moreover by using the separable measure the risk of generating new samples in decision boundaries with hard-to-learn samples is decreased. The proposed method improves the learning accuracy in three stages including; (1) removal of abnormal samples from minority class, (2) selecting the top three samples of minority class according to desired criteria and (3) generating new sample using selected samples. The experiments are conducted on five real world datasets which is taken from Iran University of Medical Science and also six different UCI datasets. Moreover, three different classifiers, four resampling algorithms and six performance evaluation measures are used to evaluate the proposed method. The reported results indicate that the proposed approach demonstrates a better or at least comparable performance compared to those of the state-of-the-art methods.

RGAN-EL: A GAN and ensemble learning-based hybrid approach for imbalanced data classification

RuleCOSI: Combination and simplification of production rules from boosted decision trees for imbalanced classification

Nowadays, dealing with imbalanced data represents a great challenge in data mining as well as in machine learning task. In this investigation, we are interested in the problem of class imbalance in Authorship Attribution (AA) task, with specific application on Arabic text data. This article proposes a new hybrid approach based on Principal Components Analysis (PCA) and Synthetic Minority Over-sampling Technique (SMOTE), which considerably improve the performances of authorship attribution on imbalanced data. The used dataset contains 7 Arabic books written by 7 different scholars, which are segmented into text segments of the same size, with an average length of 2900 words per text. The obtained results of our experiments show that the proposed approach using the SMO-SVM classifier, presents high performance in terms of authorship attribution accuracy (100%), especially with starting character-bigrams. In addition, the proposed method appears quite interesting by improving the AA performances in imbalanced datasets, mainly with function words.

A machine learning approach for hierarchical classification of software requirements

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.