Majority voting ensemble with a decision trees for business failure prediction during economic downturns

Option Decision Trees with Majority Votes

We describe an experimental study of Option Decision Trees with majority votes. Option Decision Trees generalize regular decision trees by allowing option nodes in addition to decision nodes; such nodes allow for several possible tests to be conducted instead of the commonly used single test. Our goal was to explore when option nodes are most useful and to control the growth of the trees so tha...

Profiles and Majority Voting-Based Ensemble Method for Protein Secondary Structure Prediction

Machine learning techniques have been widely applied to solve the problem of predicting protein secondary structure from the amino acid sequence. They have gained substantial success in this research area. Many methods have been used including k-Nearest Neighbors (k-NNs), Hidden Markov Models (HMMs), Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), which have attracted atte...

A Decision Support System For Handwritten Numerals Using Majority Voting

Feature extraction is an important step of pattern recognition. This study uses profile based feature extraction and uses Simple Profile (with cropping and without cropping image samples), Contour based feature extraction for recognizing handwritten numerals. The features are computed by using 48 X 48 as a feature length classifier used Back propagation neural network for the classification.. T...

A One-Vs-One Classifier Ensemble With Majority Voting for Activity Recognition

Ensemble-Trees: Leveraging Ensemble Power Inside Decision Trees

Decision trees are among the most effective and interpretable classification algorithms while ensembles techniques have been proven to alleviate problems regarding over-fitting and variance. On the other hand, decision trees show a tendency to lack stability given small changes in the data, whereas interpreting an ensemble of trees is challenging to comprehend. We propose the technique of Ensem...