Ensemble classifiers originated in the machine learning community. They work by fitting many individual classifiers and combining them by weighted or unweighted voting. The ensemble classifier is often much more accurate than the individual classifiers from which it is built. In fact, ensemble classifiers are among the most accurate general-purpose classifiers available. We introduce a new ense...

decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. the resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. the most comprehensible decision trees have been designed for perfect symbolic data. classical crisp decision trees (dt) are widely applied to classification t...

there are three major strategies to form neural network ensembles. the simplest one is the cross validation strategy in which all members are trained with the same training data. bagging and boosting strategies pro-duce perturbed sample from training data. this paper provides an ideal model based on two important factors: activation function and number of neurons in the hidden layer and based u...

The induction of classification rules from previously unseen examples is one of the most important data mining tasks in science as well as commercial applications. In order to reduce the influence of noise in the data, ensemble learners are often applied. However, most ensemble learners are based on decision tree classifiers which are affected by noise. The Random Prism classifier has recently ...

For many years, reading rate as word correct per minute (WCPM) has been investigated by researchers an indicator of learners’ level oral speed, accuracy, and comprehension. The aim the study is to predict levels WCPM using three machine learning algorithms which are Ensemble Classifier (EC), Decision Tree (DT), K- Nearest Neighbor (KNN). data this were collected from 100 Kurdish EFL students in...

This paper presents a classifier ensemble approach for predicting the survivability of the breast cancer patients using the latest database version of the Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute. The system consists of two main components; features selection and classifier ensemble components. The features selection component divides the featu...