The classification of stream waters using parameters such as fecal coliforms into the classes body contact and recreation, fishing boating, domestic utilization, danger itself is a significant practical problem water quality prediction worldwide. Various statistical causal approaches are used routinely to solve from modeling perspective. However, transparent process in form Decision Trees shed ...

There has been an increasing interest in applying machine learning methods in urban energy assessment. This research implemented six statistical learning methods in estimating domestic gas and electricity using both physical and socio-economic explanatory variables in London. The input variables include dwelling types, household tenure, household composition, council tax band, population age gr...

Decision committee learning has demonstrated spectacular success in reducing classification error from learned classifiers. These techniques develop a classifier in the form of a committee of subsidiary classifiers. The combination of outputs is usually performed by majority vote. Voting, however, has a shortcoming. It is unable to take into account local expertise. When a new instance is diffi...

The paper reports three new ensembles of supervised learning predictors for managing medical insurance costs. open dataset is used data analysis methods development. usage artificial intelligence in the management financial risks will facilitate economic wear time and money protect patients’ health. Machine associated with many expectations, but its quality determined by choosing a good algorit...

Methods for voting classiication algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classiiers for artiicial and real-world datasets. We review these algorithms and describe a large empirical study comparing several variants in conjunction with a decision tree inducer (three variants) and a Naive-Bayes inducer. The purpose of the...

The error rate of decision-tree and other classi-cation learners can often be much reduced by bagging: learning multiple models from bootstrap samples of the database, and combining them by uniform voting. In this paper we empirically test two alternative explanations for this, both based on Bayesian learning theory: (1) bagging works because it is an approximation to the optimal procedure of B...

The idea of ensemble methodology is to combine multiple predictive models in order to achieve a better prediction performance. In this task we analyze the self-adaptive methods for improving the performance of Ant Colony Decision Tree and Forest algorithms. Our goal is to present and compare new metaensemble approaches based on Ant Colony Optimization. The proposed meta-classifiers (consisting ...

Various machine learning models have been used in the biomedical engineering field, but only a small number of studies conducted on respiratory rate estimation. Unlike ensemble using simple averages basic learners such as bagging, random forest, and boosting, gradient boosting algorithm is based effective iteration strategies. This just beginning to be for Based this, we propose novel methodolo...

Purpose: The aim was studying the discriminability by ROC curves and gain charts for simple fixed combining of constituent classifiers, for asthma severity diagnosis, and also for bagging and boosting. Material and methods: ROC shows a performance over a range of relative costs and probabilities a priori. Area under ROC curve (AUC) is the measure of separability of two probability distributions...