Various tools have been developed to predict B-cell epitopes. We proposed a multistrategy approach by integrating two ensemble learning techniques, namely bagging and meta-decision tree, with a threshold-based cost-sensitive method. By exploiting the synergy among multiple retrainable inductive learners, it directly learns a tree-like classification architecture from the data, and is not limite...

Abstract—Vote-boosting is a sequential ensemble learning method in which individual classifiers are built on different weighted versions of the training data. To build a new classifier, the weight of each training instance is determined as a function of the disagreement rate of the current ensemble predictions for that particular instance. Experiments using the symmetric beta distribution as th...

Abstract Class imbalance is an important consideration for cybersecurity and machine learning. We explore classification performance in detecting web attacks the recent CSE-CIC-IDS2018 dataset. This study considers a total of eight random undersampling (RUS) ratios: no sampling, 999:1, 99:1, 95:5, 9:1, 3:1, 65:35, 1:1. Additionally, seven different classifiers are employed: Decision Tree (DT), ...

Credit scoring is an important finance activity. Both statistical techniques and Artificial Intelligence (AI) techniques have been explored for this topic. But different techniques have different advantages and disadvantages on different datasets. Recent studies draw no consistent conclusions to show that one technique is superior to the other, while they suggest combining multiple classifiers,...

In this paper, we present Deep Extreme Feature Extraction (DEFE), a new ensemble MVA method for searching ττ channel of Higgs bosons in high energy physics. DEFE can be viewed as a deep ensemble learning scheme that trains a strongly diverse set of neural feature learners without explicitly encouraging diversity and penalizing correlations. This is achieved by adopting an implicit neural contro...

Ensemble classification – combining the results of a set of base learners – has received much attention in the machine learning community and has demonstrated promising capabilities in improving classification accuracy. Compared with neural network or decision tree ensembles, there is no comprehensive empirical research in support vector machine (SVM) ensembles. To fill this void, this paper an...