Objective: To propose the most effective machine learning algorithm for predicting cardiac problems. Methods: The dataset used this study is “heart” which was taken from www.kaggle.com. heart contains 13 features and a target variable. It divided into 70 percent training set 30 testing set. K-Fold cross-validation in model evaluation selection. K value chosen ten. A Hybrid Ensemble built using ...

the work presented in this paper is dedicated to improving the methods of detection and diagnosis of faults affecting production systems, particularly photovoltaic systems. We proposed a new intelligent algorithm for the detection and diagnosis of PV installations, capable of detecting and resonate to define the type of defects that can affect this type of system. This new algorithm is based on...

Sigmoidal growths are well approximated by the non-linear sigmoidal growth models including Richards (1959) Morgan et al (1975), Davies and Ku (1977) and Muller et al (2006) among many others. This article deals with the comparison of the nonparametric regression with the non-linear regression models in order to locate the better approximation for sigmoidal growths. To unwind the standard assum...

H. Altay Güvenir and Aynur Akkuş Department of Computer Engineering and Information Science Bilkent University, 06533, Ankara, Turkey fguvenir, [email protected] Abstract. This paper proposes an extension to the k Nearest Neighbor algorithm on Feature Projections, called kNNFP. The kNNFP algorithm has been shown to achieve comparable accuracy with the well-known kNN algorithm. However, k...

Data mining is the process of extraction of hidden and useful information from huge data. It is also called knowledge discovery process from data. Bug tracking systems are made to manage bug reports, which are collected from various sources. These bug reports are needed to be labeled as security bug reports or non security bug reports. Data mining uses to apply mining algorithm to extract infor...

A set of forty one substituted 2-phenyl-benzimidazole with anti allergic activity against IgE was subjected to three dimensional quantitative structure activity relationship studies through recently introduced knearest neighbor molecular field analysis with step wise forward-backward as variable selection method to study the correlation between the molecular properties and the In-vitro IgE acti...

Classification of objects is an important area in a variety of fields and applications. Many different methods are available to make a decision in those cases. The knearest neighbor rule (k-NN) is a well-known nonparametric decision procedure. Classification rules based on the k-NN have already been proposed and applied in diverse substantive areas. The editing k-NN proposed by Wilson would be ...

Eager learners such as neural networks, decision trees, and naïve Bayes classifiers construct a single model from the training data before observing any test set instances. In contrast, lazy learners such as Knearest neighbor consider a test set instance before they generalize beyond the training data. This allows making predictions from only a specific selection of instances most similar to th...

Miller, Teng, Thurston, and Vavasis proved that every knearest neighbor graph (k-NNG) in R has a balanced vertex separator of size O(n1−1/dk1/d). Later, Spielman and Teng proved that the Fiedler value — the second smallest eigenvalue of the graph — of the Laplacian matrix of a k-NNG in R is at O( 1 n2/d ). In this paper, we extend these two results to nearest neighbor graphs in a metric space w...