This paper presents ACE (Autonomous Classification Engine), a framework for using and optimizing classifiers. Given a set of feature vectors, ACE experiments with a variety of cla...
Cory McKay, Rebecca Fiebrink, Daniel McEnnis, Bein...
Classification fusion combines multiple classifications of data into a single classification solution of greater accuracy. Feature extraction aims to reduce the computational cost ...
Behrouz Minaei-Bidgoli, Gerd Kortemeyer, William F...
We propose a well-founded method of ranking a pool of m trained classifiers by their suitability for the current input of n instances. It can be used when dynamically selecting a s...
Many ensemble methods, such as Bagging, Boosting, Random Forest, etc, have been proposed and widely used in real world applications. Some of them are better than others on noisefre...
Background: The most common substitution matrices currently used (BLOSUM and PAM) are based on protein sequences with average amino acid distributions, thus they do not represent ...