Comparative performance analysis of machine learning classifiers on ovarian cancer dataset

2017 ◽  
Author(s):  
Sharmistha Bhattacharjee ◽  
Yumnam Jayanta Singh ◽  
Dipankar Ray
Keyword(s):  
Machine Learning ◽  
Ovarian Cancer ◽  
Performance Analysis ◽  
Cancer Dataset ◽  
Comparative Performance ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

scholarly journals Comparative Performance Analysis of Machine Learning Classifiers in Detection of Childhood Pneumonia Using Chest Radiographs

2013 ◽  
Vol 18 ◽  
pp. 2579-2582 ◽  
Author(s):  
Rafael T. Sousa ◽  
Oge Marques ◽  
Fabrizzio Alphonsus A.M.N. Soares ◽  
Iwens I.G. Sene ◽  
Leandro L.G. de Oliveira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Chest Radiographs ◽  
Comparative Performance ◽  
Childhood Pneumonia ◽  
Machine Learning Classifiers ◽  
Learning Classifiers

scholarly journals Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 16033-16048 ◽  
Author(s):  
Khawaja Moyeezullah Ghori ◽  
Rabeeh Ayaz Abbasi ◽  
Muhammad Awais ◽  
Muhammad Imran ◽  
Ata Ullah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Analysis ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Different Types

A Comparison of Machine Learning Classifiers

2011 ◽  
Vol 271-273 ◽  
pp. 149-153 ◽  
Author(s):  
Phani Srikanth ◽  
Amarjot Singh ◽  
Devinder Kumar ◽  
Aditya Nagrare ◽  
Vivek Angoth
Keyword(s):  
Machine Learning ◽  
Time Analysis ◽  
Cancer Dataset ◽  
Classification Techniques ◽  
Training Time ◽  
Machine Learning Classification ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Precision And Accuracy

A number of different classifiers have been used to improve the precision and accuracy and give better classification results. Machine learning classifiers have proven to be the most successful techniques in majority of the fields. This paper presents a comparison of the three most successful machine learning classification techniques SVM, boosting and Local SVM applied to a cancer dataset. The comparison is made on the basis of precision and accuracy along with the training time analysis. Finally, the efficacy of the classifiers is found.

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