Classifying Documents with Respect to “Earnings” and Then Making a Predictive Model for the Target Variable Using Decision Trees, MARSplines, Naïve Bayes Classifier, and K-Nearest Neighbors with STATISTICA Text Miner

2012 ◽  
pp. 773-796
Keyword(s):  
Predictive Model ◽  
Decision Trees ◽  
Naive Bayes ◽  
Nearest Neighbors ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Target Variable ◽  
K Nearest Neighbors

Detecting Malicious Defects in 3D Printing Process Using Machine Learning and Image Classification

2016 ◽  
Author(s):  
Mingtao Wu ◽  
Vir V. Phoha ◽  
Young B. Moon ◽  
Amith K. Belman
Keyword(s):  
Machine Learning ◽  
3D Printing ◽  
Image Classification ◽  
Decision Trees ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Printing Process

3D printing, or additive manufacturing, is a key technology for future manufacturing systems. However, 3D printing systems have unique vulnerabilities presented by the ability to affect the infill without affecting the exterior. In order to detect malicious infill defects in 3D printing process, this paper proposes the following: 1) investigate malicious defects in the 3D printing process, 2) extract features based on simulated 3D printing process images, and 3) an experiment of image classification with one group of non-defect infill image and the other group of defect infill training image from 3D printing process. The images are captured layer by layer from the top view of software simulation preview. The data extracted from images is input to two machine learning algorithms, Naive Bayes Classifier and J48 Decision Trees. The result shows Naive Bayes Classifier has an accuracy of 85.26% and J48 Decision Trees has an accuracy of 95.51% for classification.

scholarly journals COMPATIBILITY OF SELECTION OF STUDENT DEPARTMENTS USING k-NEAREST NEIGHBOR AND NAÏVE BAYES CLASSIFIER IN INFORMATICS PRIVATE VOCATIONAL SCHOOL, SERANG CITY

2021 ◽  
Vol 4 (1) ◽  
pp. 33-39
Author(s):  
Budi Pangestu ◽  
Keyword(s):  
Nearest Neighbor ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
K Nearest Neighbor ◽  
Prospective Students ◽  
Naïve Bayes Classifier ◽  
K Nearest Neighbors ◽  
Selection Of

Selection of majors by prospective students when registering at a school, especially a Vocational High School, is very vulnerable because prospective students usually choose a major not because of their individual wishes. And because of the increasing emergence of new schools in cities and districts in each province in Indonesia, especially in the province of Banten. Problems experienced by prospective students when choosing the wrong department or not because of their desire, so that it has an unsatisfactory value or value in each semester fluctuates, especially in their Productive Lessons or Competencies. To provide a solution, a departmental suitability system is needed that can provide recommendations for specialization or major suitability based on students' abilities through attributes that can later assist students in the suitability of majors. The process of classifying the suitability of majors in data mining uses the k-Nearest Neighbor and Naive Bayes Classifier methods by entering 16 (sixteen) criteria or attributes which can later provide an assessment of students through this test when determining the majors for themselves, and there is no interference from people. another when choosing a major later. Research that has been carried out successfully using the k-Nearest Neighbors method has a higher recall of 99%, 81% accuracy and 82% precision compared to the Naïve Bayes Classifier whose recall only yields 98% while the accuracy and precision is the same as the k- Nearest Neighbors.

A FILTER-WRAPPER METHOD TO SELECT VARIABLES FOR THE NAIVE BAYES CLASSIFIER BASED ON CREDAL DECISION TREES

2009 ◽  
Vol 17 (06) ◽  
pp. 833-854 ◽  
Author(s):  
JOAQUÍN ABELLÁN ◽  
ANDRÉS R. MASEGOSA
Keyword(s):  
Decision Trees ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Selection Methods ◽  
Naïve Bayes Classifier ◽  
Main Class ◽  
Wrapper Method ◽  
Split Criterion

Variable selection methods play an important role in the field of attribute mining. In the last few years, several feature selection methods have appeared showing that the use of a set of decision trees learnt from a database can be a useful tool for selecting relevant and informative variables regarding a main class variable. With the Naive Bayes classifier as reference, in this article, our aims are twofold: (1) to study what split criterion has better performance when a complete decision tree is used to select variables; and (2) to present a filter-wrapper selection method using decision trees built with the best possible split criterion obtained in (1).

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