Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

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 is central to modern data science, from exploratory data analysis to
predictive model-building. The" stability" of a feature selection algorithm refers to the …

Background Some of the current software tools for comparative metagenomics provide
ecologists with the ability to investigate and explore bacterial communities using α–& β …

Feature subset selection can be used to sieve through large volumes of data and discover
the most informative subset of variables for a particular learning problem. Yet, due to …

In the past decade, big data has become increasingly prevalent in a large number of
applications. As a result, datasets suffering from noise and redundancy issues have …

Ensemble methods are often used to decide on a good selection of features for later
processing by a classifier. Examples of this are in the determination of Random Forest …

This research developed and tested a filter algorithm that serves to reduce the feature space
in healthcare datasets. The algorithm binarizes the dataset, and then separately evaluates …

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 …

A typical assumption made in machine learning is that a learning model does not consider
an adversary's existence that can subvert a classifier's objective. As a result, machine …