The electrocardiogram (ECG) signal has great importance in diagnosing cardiac arrhythmias. In this paper we have compared three classifiers on the basis of their accuracies for the detection of arrhythmia. The algorithms that are used for classification are supervised machine learning algorithm. The performance of the classifier depends upon its accuracy rate. The classifiers used are Nearest N...

This paper proposes a novel methodology for very short term forecasting of hourly global solar irradiance (GSI). The proposed methodology is based on meteorology data, especially for optimizing the operation of power generating electricity from photovoltaic (PV) energy. This methodology is a combination of k-nearest neighbor (k-NN) algorithm modelling and artificial neural network (ANN) model. ...

Forest surveys provide critical information for many diverse interests. Data are often collected from samples, and from these samples, maps of resources and estimates of aerial totals or averages are required. In this paper, two approaches for mapping and estimating totals; the spatial linear model (SLM) and k-NN (k-Nearest Neighbor) are compared, theoretically, through simulations, and as appl...

In this paper comparison is done between k-nearest neighbor and naïve basin classifier based on the subset of features. Sequential feature selection method is used to establish the subsets. Four categories of subsets are used like life and medical transcripts, arts and humanities transcripts, social science transcripts, physical science transcripts to show the experimental results to classify d...

Klasifikasi merupakan serangkaian proses pembentukan model dari suatu objek ke dalam kelompok untuk memprediksi kelas yang belum diketahui sebelumnya. Modified K-Nearest Neighbor (MK-NN) salah satu metode klasifikasi pengembangan algoritma (K-NN) menambahkan validitas serta weight voting (pembobotan) mengatasi tingkat akurasi rendah K-NN. Penelitian ini bertujuan mengetahui hasil pengklasifikas...

1 July 11, 2016 We use the notation x = g−1xg. Theorem 1.1. Let G be a finite group, and p be a prime. Suppose further that P is a p-group that acts by automorphisms on G, and p |G|. Consider the P -invariant classes of G: clP (G) = {K : K is a conjugacy class of G and K = K for all x ∈ P} Let C = CG(P ) be the fixed point subgroup of G under the action of P . Then the map clP (G) −→ cl(C) : K ...

This paper presents an interval type-2 fuzzy Knearest neighbor (NN) algorithm that is an extension of the type1 fuzzy K-NN algorithm proposed in [l]. In our proposed method, the membership values for each vector are 'extended as interval type-2 fuzzy memberships by assigning uncertainty to the type-1 memberships. By doing so, the classification result obtained by the interval type-2 fuzzy K-NN ...

This paper presents average-case analyses of instance-based learning algorithms. The algorithms analyzed employ a variant of k-nearest neighbor classi-er (k-NN). Our analysis deals with a monotone m-of-n target concept with irrelevant attributes, and handles three types of noise: relevant attribute noise, irrelevant attribute noise, and class noise. We formally represent the expected classi-cat...

The energy dependence of the total kaon production cross sections in baryon baryon (N and ∆) collisions are studied in the resonance model, which is a relativistic, tree-level treatment. This study is the first attempt to complete a systematic, consistent investigation of the elementary kaon production reactions for both the pion baryon and baryon baryon reactions. Our model suggests that the m...

The K-nearest neighbor (K-NN) classification method originally developed in the probabilistic framework has serious difficulties to classify correctly the close data points (objects) originating from different classes. To cope with such difficult problem and make the classification result more robust to misclassification errors, we propose a new belief-based K-nearest neighbor (BK-NN) method th...