Voting: A machine learning approach

Debt Collection Industry: Machine Learning Approach

Businesses are increasingly interested in how big data, artificial intelligence, machine learning, and predictive analytics can be used to increase revenue, lower costs, and improve their business processes. In this paper, we describe how we have developed a data-driven machine learning method to optimize the collection process for a debt collection agency. Precisely speaking, we create a frame...

A Machine Learning Based Approach to Fiber Tractography Using Classifier Voting

Current tractography pipelines incorporate several modelling assumptions about the nature of the diffusion-weighted signal. We present an approach that tracks fiber pathways based on a random forest classification and voting process, guiding each step of the streamline progression by directly processing raw signal intensities. We evaluated our approach quantitatively and qualitatively using pha...

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...

Machine teaching: a machine learning approach to technology enhanced learning

Many applications of Technology Enhanced Learning are based on strong assumptions: Knowledge needs to be standardized, structured and most of all externalized into learning material that preferably is annotated with meta-data for efficient re-use. A vast body of valuable knowledge does not meet these assumptions, including informal knowledge such as experience and intuition that is key to many ...

Machine Learning A Probabilistic Approach