Naive Bayes combined with partial least squares for classification of high dimensional microarray data

2022 ◽  
pp. 104492
Author(s):  
Tahir Mehmood ◽  
Arzoo Kanwal ◽  
Muhammad Moeen Butt
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Microarray Data ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
High Dimensional

scholarly journals Kernelized partial least squares for feature reduction and classification of gene microarray data

2011 ◽  
Vol 5 (Suppl 3) ◽  
pp. S13 ◽  
Author(s):  
Walker H Land ◽  
Xingye Qiao ◽  
Daniel E Margolis ◽  
William S Ford ◽  
Christopher T Paquette ◽  
...  
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Microarray Data ◽  
Feature Reduction ◽  
Gene Microarray

Partial least squares-discriminant analysis (PLS-DA) for classification of high-dimensional (HD) data: a review of contemporary practice strategies and knowledge gaps

The Analyst ◽  
2018 ◽  
Vol 143 (15) ◽  
pp. 3526-3539 ◽  
Author(s):  
Loong Chuen Lee ◽  
Choong-Yeun Liong ◽  
Abdul Aziz Jemain
Keyword(s):  
Discriminant Analysis ◽  
Least Squares ◽  
Partial Least Squares ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Knowledge Gaps ◽  
Contemporary Practice

This review highlights and discusses critically various knowledge gaps in classification modelling using PLS-DA for high dimensional data.

Perbandingan Optimasi Feature Selection pada Naïve Bayes untuk Klasifikasi Kepuasan Airline Passenger

2021 ◽  
Vol 5 (3) ◽  
pp. 527-533
Author(s):  
Yoga Religia ◽  
Amali Amali
Keyword(s):  
Feature Selection ◽  
Customer Satisfaction ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Point Of View ◽  
Classification Model ◽  
Passenger Satisfaction ◽  
Airline Passenger ◽  
Bayes Algorithm

The quality of an airline's services cannot be measured from the company's point of view, but must be seen from the point of view of customer satisfaction. Data mining techniques make it possible to predict airline customer satisfaction with a classification model. The Naïve Bayes algorithm has demonstrated outstanding classification accuracy, but currently independent assumptions are rarely discussed. Some literature suggests the use of attribute weighting to reduce independent assumptions, which can be done using particle swarm optimization (PSO) and genetic algorithm (GA) through feature selection. This study conducted a comparison of PSO and GA optimization on Naïve Bayes for the classification of Airline Passenger Satisfaction data taken from www.kaggle.com. After testing, the best performance is obtained from the model formed, namely the classification of Airline Passenger Satisfaction data using the Naïve Bayes algorithm with PSO optimization, where the accuracy value is 86.13%, the precision value is 87.90%, the recall value is 87.29%, and the value is AUC of 0.923.

Varying Naïve Bayes Models With Applications to Classification of Chinese Text Documents

2014 ◽  
Vol 32 (3) ◽  
pp. 445-456 ◽  
Author(s):  
Guoyu Guan ◽  
Jianhua Guo ◽  
Hansheng Wang
Keyword(s):  
Chinese Text ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Text Documents

scholarly journals Peringkasan dan Support Vector Machine pada Klasifikasi Dokumen

2017 ◽  
Vol 9 (4) ◽  
pp. 416 ◽  
Author(s):  
Nelly Indriani Widiastuti ◽  
Ednawati Rainarli ◽  
Kania Evita Dewi
Keyword(s):  
Support Vector Machine ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Training Data ◽  
Support Vector ◽  
Good Reputation ◽  
Multiclass Support Vector Machine ◽  
Simple Logistic ◽  
Better Than

Classification is the process of grouping objects that have the same features or characteristics into several classes. The automatic documents classification use words frequency that appears on training data as features. The large number of documents cause the number of words that appears as a feature will increase. Therefore, summaries are chosen to reduce the number of words that used in classification. The classification uses multiclass Support Vector Machine (SVM) method. SVM was considered to have a good reputation in the classification. This research tests the effect of summary as selection features into documents classification. The summaries reduce text into 50%. A result obtained that the summaries did not affect value accuracy of classification of documents that use SVM. But, summaries improve the accuracy of Simple Logistic Classifier. The classification testing shows that the accuracy of Naïve Bayes Multinomial (NBM) better than SVM

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