A variety of applications employ ensemble learning models, using a collection of decision
trees, to quickly and accurately classify an input based on its vector of features. In this paper …

The goal of designing an ensemble of simple classifiers is to improve the accuracy of a
recognition system. However, the performance of ensemble methods is problem-dependent …

Unconstrained handwritten text recognition is one of the most difficult problems in the field of
pattern recognition. Recently, a number of classifier creation and combination methods …

Various measures, such as Margin and Bias/Variance, have been proposed with the aim of
gaining a better understanding of why Multiple Classifier Systems (MCS) perform as well as …

To detect the improper sitting posture of a person sitting on a chair, a posture detection
system using machine learning classification has been proposed in this work. The …

The ensemble method is a powerful data mining paradigm, which builds a classification
model by integrating multiple diversified component learners. Bagging is one of the most …

In some real-world problems solved by machine learning it is compulsory for the solution
provided to be comprehensible so that the correct decision can be made. It is in this context …

When different subsamples of the same data set are used to induce classification trees, the
structure of the built classifiers is very different. The stability of the structure of the tree is of …

The current study delineates the effect of Odia and Tamil music on the Autonomic Nervous
System (ANS) and cardiac conduction pathway of Odia volunteers. The analysis of the ECG …

In this paper, we propose the scheme of multiple input representation-adaptive ensemble
generation and aggregation (MIR-AEGA) for the classification of time series data. MIR-AEGA …