Feature selection technique is a knowledge discovery tool which provides an understanding
of the problem through the analysis of the most relevant features. Feature selection aims at …

Ensemble learning is a prolific field in Machine Learning since it is based on the assumption
that combining the output of multiple models is better than using a single model, and it …

Feature selection has been an important research area in data mining, which chooses a
subset of relevant features for use in the model building. This paper aims to provide an …

Modern healthcare is getting reshaped by growing Electronic Medical Records (EMR).
Recently, these records have been shown of great value towards building clinical prediction …

Nowadays, a huge amount of data is generated every day in continuous manner in every
hour and if the data is not utilized in the right or meaningful manner then this is just like …

With increasing concerns of climate change, renewable resources such as photovoltaic (PV)
have gained popularity as a means of energy generation. The smooth integration of such …

Unwanted bias is a major concern in machine learning, raising in particular significant
ethical issues when machine learning models are deployed within high-stakes decision …

Faulty compressors must be detected in advance to speed up the quality control process of
the compressor's performance. Machine learning models have recently been used as fault …

Feature selection plays a significant role in computer science; nevertheless, this task is
intractable since its search space scales exponentially with the number of dimensions …

Increasingly, many machine learning applications are now associated with very large data
sets whose sizes were almost unimaginable just a short time ago. As a result, many of the …