Use of Linear Discriminant Analysis (LDA), K Nearest Neighbours (KNN), Decision Tree (CART), Random Forest (RF), Gaussian Naive Bayes (NB), Support Vector Machines (SVM) to Predict Admission for Post Graduation Courses

2020 ◽  
Author(s):  
Ivan Rodrigues ◽  
Alitta Parayil ◽  
Tarun Shetty ◽  
Imran Mirza
Keyword(s):  
Support Vector Machines ◽  
Discriminant Analysis ◽  
Random Forest ◽  
Decision Tree ◽  
Linear Discriminant Analysis ◽  
Naive Bayes ◽  
Support Vector ◽  
Linear Discriminant ◽  
Nearest Neighbours ◽  
Vector Machines

scholarly journals Discriminating Quasars from Stars Based on SDSS and UKIDSS Databases

2012 ◽  
Vol 8 (S295) ◽  
pp. 180-180
Author(s):  
He Ma ◽  
Yanxia Zhang ◽  
Yongheng Zhao ◽  
Bo Zhang
Keyword(s):  
Support Vector Machines ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Experimental Result ◽  
Support Vector ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Joint Approach

AbstractIn this work, two different algorithms: Linear Discriminant Analysis (LDA) and Support Vector Machines (SVMs) are combined for the classification of unresolved sources from SDSS DR8 and UKIDSS DR8. The experimental result shows that this joint approach is effective for our case.

scholarly journals Linear discriminant analysis and support vector machines for classifying breast cancer

2021 ◽  
Vol 10 (1) ◽  
pp. 253
Author(s):  
Zuherman Rustam ◽  
Yasirly Amalia ◽  
Sri Hartini ◽  
Glori Stephani Saragih
Keyword(s):  
Breast Cancer ◽  
Support Vector Machines ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Linear Discriminant ◽  
Specialist Doctor ◽  
Vector Machines

<span id="docs-internal-guid-4db59d91-7fff-c659-478a-6dd7456f380f"><span>Breast cancer is an abnormal cell growth in the breast that keeps changed uncontrolled and it forms a tumor. The tumor can be benign or malignant. Benign could not be dangerous to health and cancerous, but malignant could be has a probability dangerous to health and be cancerous. A specialist doctor will diagnose the patient and give treatment based on the diagnosis which is benign or malignant. Machine learning offer times efficiency to determine a cancer cell. The machine will learn the pattern based on the information from the dataset. Support vector machines and linear discriminant analysis are common methods that can be used in the classification of cancer. In this study, both of linear discriminant analysis and support vector machines are compared by looking from accuracy, sensitivity, specificity, and F1-score. We will know which methods are better in classifying breast cancer dataset. The result shows that the support vector machine has better performance than the linear discriminant analysis. It can be seen from the accuracy is 98.77%.</span></span>

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