A Comparison between Optimum-Path Forest and k-Nearest Neighbors Classifiers (original) (raw)

K Nearest Neighbor Edition to Guide Classification Tree Learning: Motivation and Experimental Results

2006

This paper presents a new hybrid classifier that combines the Nearest Neighbor distance based algorithm with the Classification Tree paradigm. The Nearest Neighbor algorithm is used as a preprocessing algorithm in order to obtain a modified training database for the posterior learning of the classification tree structure; experimental section shows the results obtained by the new algorithm; comparing these results with those obtained by the classification trees when induced from the original training data we obtain that the new approach performs better or equal according to the Wilcoxon signed rank statistical test.

Comparison of the performance of GaussianNB Algorithm, the K Neighbors Classifier Algorithm, the Logistic Regression Algorithm, the Linear Discriminant Analysis Algorithm, and the Decision Tree Classifier Algorithm on same dataset

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022

Most educational institutions worldwide have been closed since March 2020 in an effort to slow the spread of the Covid-19 epidemic. More than 90% of students around the world have been influenced by this. In this study, we'll make a prediction about whether or not the Covid-19 epidemic has benefited student performance. Our data will be divided into training and testing datasets, with 80% of the data utilised for training and 20% for testing. To calculate the accuracy of our predictions, we'll use six different algorithms, including the RandomForestClassifier Algorithm, the GaussianNB Algorithm, the K Neighbors Classifier Algorithm, the Logistic Regression Algorithm, the Linear Discriminant Analysis Algorithm, and the DecisionTree Classifier Algorithm.