Random undersampling and oversampling are simple but well-known resampling methods applied to solve the problem of class imbalance. In this paper we show that the random oversampling method can produce better classification results than the random undersampling method, since the oversampling can increase the minority class recognition rate by sacrificing less amount of majority class recognition rate than the undersampling method. However, the random oversampling method would increase the computational cost associated with the SVM training largely due to the addition of new training examples. In this paper we present an investigation carried out to develop efficient resampling methods that can produce comparable classification results to the random oversampling results, but with the use of less amount of data. The main idea of the proposed methods is to first select the most informative data examples located closer to the class boundary region by using the separating hyperplane found by training an SVM model on the original imbalanced dataset, and then use only those examples in resampling. We demonstrate that it would be possible to obtain comparable classification results to the random oversampling results through two sets of efficient resampling methods which use 50% less amount of data and 75% less amount of data, respectively, compared to the sizes of the datasets generated by the random oversampling method.

Support vector machines (SVMs) is a popular machine learning technique, which works effectively with balanced datasets. However, when it comes to imbalanced datasets, SVMs produce suboptimal classification models. On the other hand, the SVM algorithm is sensitive to outliers and noise present in the datasets. Therefore, although the existing class imbalance learning (CIL) methods can make SVMs less sensitive to class imbalance, they can still suffer from the problem of outliers and noise. Fuzzy SVMs (FSVMs) is a variant of the SVM algorithm, which has been proposed to handle the problem of outliers and noise. In FSVMs, training examples are assigned different fuzzy-membership values based on their importance, and these membership values are incorporated into the SVM learning algorithm to make it less sensitive to outliers and noise. However, like the normal SVM algorithm, FSVMs can also suffer from the problem of class imbalance. In this paper, we present a method to improve FSVMs for CIL (called FSVM-CIL), which can be used to handle the class imbalance problem in the presence of outliers and noise. We thoroughly evaluated the proposed FSVM-CIL method on ten real-world imbalanced datasets and compared its performance with five existing CIL methods, which are available for normal SVM training. Based on the overall results, we can conclude that the proposed FSVM-CIL method is a very effective method for CIL, especially in the presence of outliers and noise in datasets.

In class imbalance learning, the performance measure used for the model selection would play a vital role. It has been well-studied in the past research that the most widely used performance measure, the overall accuracy of the model, can lead to sub-optimal classification models when learning from imbalanced datasets. In order to overcome this problem, other performance measures, such as the geometric-mean (Gm) and F-measure (Fm), have been used for imbalanced dataset learning. Training a classifier system with an imbalanced dataset (where the positive class is the minority class) would usually produce sub-optimal models having a higher specificity (SP) and a lower sensitivity (SE). By applying class imbalance learning methods, we would often be able to increase the SE by sacrificing some amount of SP. In some type of real world imbalanced classification problems, such as the gene finding Bioinformatics problems, it is important to improve the SE as much as possible by keeping the reduction of SP to the minimum. In this paper, we show that with respect to this type of classification problems the existing performance measures used in class imbalance learning (Gm and Fm) can still result in sub-optimal classification models. In order to circumvent these problems, we introduced a new performance measure, called adjusted geometric-mean (AGm). We show, both analytically and empirically on two real-world Bioinformatics datasets, that AGm can perform better than Gm and Fm metrics.

Abstract Motivation: In this article, we show that the classification of human precursor microRNA (pre-miRNAs) hairpins from both genome pseudo hairpins and other non-coding RNAs (ncRNAs) is a common and essential requirement for both comparative and non-comparative computational recognition of human miRNA genes. However, the existing computational methods do not address this issue completely or successfully. Here we present the development of an effective classifier system (named as microPred) for this classification problem by using appropriate machine learning techniques. Our approach includes the introduction of more representative datasets, extraction of new biologically relevant features, feature selection, handling of class imbalance problem in the datasets and extensive classifier performance evaluation via systematic cross-validation methods. Results: Our microPred classifier yielded higher and, especially, much more reliable classification results in terms of both sensitivity (90.02%) and specificity (97.28%) than the exiting pre-miRNA classification methods. When validated with 6095 non-human animal pre-miRNAs and 139 virus pre-miRNAs from miRBase, microPred resulted in 92.71% (5651/6095) and 94.24% (131/139) recognition rates, respectively. Availability: The microPred classifier, the datasets used, and the features extracted are freely available at http://web.comlab.ox.ac.uk/people/ManoharaRukshan.Batuwita/microPred.htm. Contact: [email protected]; [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

micoRNA (miRNA) is a vital class of non-coding RNA genes, which participates in post-transcriptional gene regulation in eukaryotic cell. Interestingly, some close relationships between miRNA expression levels and several human diseases like cancers have been recently uncovered. Difficulties of identifying miRNAs via direct experimental method due to their special and temporal expression patterns make the computational prediction methods paramount important. Specially, non-comparative computational methods would have the advantage of recognizing species-specific miRNAs that can be missed by comparative methods. In this paper we present a systematic development of an improved classifier system for non-comparative human miRNA gene recognition using effective machine learning techniques.

Fuzzy logic plays a vital role in handwriting recognition, since a fuzzy character recognition system with an automatically generated rule base possesses the features of flexibility, efficiency and online adaptability. One of the major requirements of such a fuzzy system is the segmentation of individual characters into meaningful segments. Then these segments can be used for the extraction of fuzzy features of the handwritten characters. This paper describes two algorithms for the meaningful segmentation of individual offline handwritten numeric skeletons.