Our algorithm, named Clus-DTI, makes use of well-known machine learning algorithms, namely k-means [11], Expectation Maximization [12] and C4.5 [1].
Influencing variables were identified with the help of a most recent and most promising machine learning technique, namely random forest.
The method was created using hybrid machine learning approach, namely Genetic algorithm (GA) coupled with artificial neutral networks (ANN).
Eight important road attributes affecting the bicycle service quality were identified using the most recent and most promising machine learning technique namely, random forest.
Machine learning algorithms, namely Multiple Linear Regression, Artificial Neural Network, REPTree and Random Tree classifiers were implemented on the dataset to predict the risk level of applicants.
Grahn et al. presents a parallel version of the Random Forest machine learning algorithm namely CudaRF [17] which was implemented using the Compute Unified Device Architecture (CUDA).
The first source is the dataset itself, providing the top level example hypotheses and the second is a machine learning algorithm, namely a Decision Tree, producing structural hypotheses.
Using all the data several classifiers were built using two machine learning techniques namely Support Vector Machines (SVM) and Neural Networks (NN).
We introduced a machine learning approach, namely an ensemble of classifiers, to solve a gas discrimination problem over extended periods of time with high accuracy rates.
This paper proposes a novel approach for applying machine learning models, namely classification and regression trees, to design a laser cutting process.
