One of the fundamental challenges for data mining is to enable inductive learning algorithms to operate on very large databases. Ensemble learning techniques such as bagging have been applied successfully to improve accuracy of classification models by generating multiple models, from replicate training sets, and aggregating them to form a composite model. In this paper, we adapt the bagging ap...

Purpose: Code smells are residuals of technical debt induced by the developers. They hinder evolution, adaptability and maintenance software. Meanwhile, they very beneficial in indicating loopholes problems bugs Machine learning has been extensively used to predict Smells research. The current study aims optimise prediction using Ensemble Learning Feature Selection techniques on three open-sour...

The current file systems are hierarchical, which can cause duplicate storage and cannot represent human’s mind map. In this paper, we explore the possibility of a heuristic, relational personal file system. Regarding each file as a node in the graph, we implement K-means, EM, LDA and Tree Bagging algorithms respectively to group the related files. In this way, we convert the current hierarchica...

In this paper, a novel random forest (RF)-based multifidelity machine learning (ML) algorithm to predict the high-fidelity Reynolds-averaged Navier-Stokes (RANS) flow field is proposed. The RF ML used increase fidelity of low-fidelity potential field. Three cases are studied, first two consist past backward-facing step, and third, subsonic around an airfoil . case, data generated using ten diff...

Based on the structural risk minimization principle, the linear SVM aiming at finding the linear decision plane with the maximal margin in the input space has gained increasing popularity due to its generalizability, efficiency and acceptable performance. However, rarely training data are evenly distributed in the input space [1], which leads to a high global VC confidence [3], downgrading the ...

Coronary Artery Disease (CAD) is one of the most prevalent diseases, which can lead to disability and sometimes even death. Diagnostic procedures of CAD are typically invasive, although they do not satisfy the required accuracy. Hence machine learning methods can be used, so that diagnosis can be made faster and with improved accuracy. There are many features that need to be taken into consider...

This paper presents a supervised machine learning approach that uses a machine learning algorithm called Random Forest for recognition of Bengali noun-noun compounds as multiword expression (MWE) from Bengali corpus. Our proposed approach to MWE recognition has two steps: (1) extraction of candidate multi-word expressions using Chunk information and various heuristic rules and (2) training the ...

For power suppliers, an important task is to accurately predict the short-term load. Thus many papers have introduced different kinds of artificial intelligent models to improve the prediction accuracy. In recent years, Random Forest Regression (RFR) and Support Vector Machine (SVM) are widely used for this purpose. However, they can not perform well when the sample data set is too noisy or wit...