Despite significant successes achieved in knowledge discovery, traditional machine
learning methods may fail to obtain satisfactory performances when dealing with complex …

A random forest (RF) classifier is an ensemble classifier that produces multiple decision
trees, using a randomly selected subset of training samples and variables. This classifier …

High-dimensional data analysis is a challenge for researchers and engineers in the fields of
machine learning and data mining. Feature selection provides an effective way to solve this …

Ensemble methods use multiple models to get better performance. Ensemble methods have
been used in multiple research fields such as computational intelligence, statistics and …

Load demand forecasting is a critical process in the planning of electric utilities. An
ensemble method composed of Empirical Mode Decomposition (EMD) algorithm and deep …

Phishing is a cyber-attack which targets naive online users tricking into revealing sensitive
information such as username, password, social security number or credit card number etc …

Despite the impressive performance of random forests (RF), its theoretical properties have
not been thoroughly understood. In this paper, we propose a novel RF framework, dubbed …

Quantifying the uncertainty of supervised learning models plays an important role in making
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …

Background Hepatitis is an inflammation of the liver, most commonly caused by a viral
infection. Supervised data mining techniques have been successful in hepatitis disease …

Mechanical properties of rocks can significantly affect energy resource recovery and
development. Uniaxial compressive strength (UCS) and Young's modulus (E) are key …