Ant System and Weighted Voting Method for Multiple Classifier Systems

Application of the Evolutionary Algorithms for Classifier Selection in Multiple Classifier Systems with Majority Voting

In many pattern recognition tasks, an approach based on combining classifiers has shown a significant potential gain in comparison to the performance of an individual best classifier. This improvement turned out to be subject to a sufficient level of diversity exhibited among classifiers, which in general can be assumed as a selective property of classifier subsets. Given a large number of clas...

Mathematics of Voting – Weighted Voting Systems

CNG Method with Weighted Voting

CNG Method for Authorship Attribution. The Common N-Grams (CNG) classification method for authorship attribution (AATT) was described in [2]. The method is based on extracting the most frequent byte n-grams of size n from the training data. The n-grams are sorted by their normalized frequency, and the first L most-frequent n-grams define an author profile. Given a test document, the test profil...

Sparse weighted voting classifier selection and its linear programming relaxations

Article history: Received 14 January 2011 Received in revised form 8 March 2012 Accepted 8 March 2012 Available online 9 March 2012 Communicated by W.-L. Hsu

Multiple Species Weighted Voting - A Genetics-Based Machine Learning System

Multiple Species Weighted Voting (MSWV) is a genetics-based machine learning (GBML) system with relatively few parameters that combines N two-class classifiers into an N -class classifier. MSWV uses two levels of speciation, one manual (a separate species is assigned to each two-class classifier) and one automatic, to reduce the size of the search space and also increase the accuracy of the dec...