The goal of this paper is to catalog the software defect prediction using machine learning. Over the last few years, the eld of software defect prediction has been extensively studied because of it's crucial position in the area of software reliability maintenance, software cost estimation and software quality assurance. An insurmountable problem associated with software defect prediction is th...

Predicting enzyme subfamily class is an imbalance multi-class classification problem due to the fact that the number of proteins in each subfamily makes a great difference. In this paper, we focus on developing the computational methods specially designed for the imbalance multi-class classification problem to predict enzyme subfamily class. We compare two support vector machine (SVM)-based met...

Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing learning algorithms maximise the classification accuracy by correctly classifying the majority class, but misclassify the minority class. However, the minority class instances are representing the concept with greater in...

Class-imbalance is one of the major challenges in real world datasets, where a few classes (called majority classes) constitute much more data samples than rest minority classes). Learning deep neural networks using such datasets leads to performances that are typically biased towards classes. Most prior works try solve class-imbalance by assigning weights various manners (e.g., re-sampling, co...

Conserved motifs in biological sequences are closely related to their structure and functions. Recently, discriminative motif discovery methods have attracted more and more attention. However, little attention has been devoted to the data imbalance problem, which is one of the main reasons affecting the performance of the discriminative models. In this article, a simulated evolution method is a...