Parameter tuning of large scale support vector machines using ensemble learning with applications to imbalanced data sets

The paper deals with problems that imbalanced and overlapping datasets often en- counter. Performance indicators as accuracy, precision and recall of imbalanced data sets, both with and without overlapping, are discussed and compared with the same performance indicators of balanced datasets with overlapping. Three popular classification algorithms, namely, Decision Tree, KNN (k-Nearest Neighbors) and SVM (Support Vector Machines) classifiers are analyzed and compared.

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Large-scale pinball twin support vector machines

Machine Learning ◽

10.1007/s10994-021-06061-z ◽

2021 ◽

Author(s):

M. Tanveer ◽

A. Tiwari ◽

R. Choudhary ◽

M. A. Ganaie

Keyword(s):

Support Vector Machines ◽

Large Scale ◽

Support Vector ◽

Twin Support Vector Machines ◽

Vector Machines

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Approximate Parameter Tuning of Support Vector Machines

2011 3rd International Workshop on Intelligent Systems and Applications ◽

10.1109/isa.2011.5873377 ◽

2011 ◽

Author(s):

Shizhong Liao ◽

Chenhao Yang ◽

Lizhong Ding

Keyword(s):

Support Vector Machines ◽

Parameter Tuning ◽

Support Vector ◽

Vector Machines

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Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting

Health Informatics Journal ◽

10.1177/1460458216656471 ◽

2016 ◽

Vol 24 (1) ◽

pp. 24-42 ◽

Cited By ~ 11

Author(s):

Claudia Ehrentraut ◽

Markus Ekholm ◽

Hideyuki Tanushi ◽

Jörg Tiedemann ◽

Hercules Dalianis

Keyword(s):

Support Vector Machines ◽

Significant Risk ◽

Parameter Tuning ◽

Hospital Staff ◽

Support Vector ◽

Patient Records ◽

Hospital Acquired Infections ◽

Vector Machines ◽

Patient Health ◽

Hospital Acquired

Hospital-acquired infections pose a significant risk to patient health, while their surveillance is an additional workload for hospital staff. Our overall aim is to build a surveillance system that reliably detects all patient records that potentially include hospital-acquired infections. This is to reduce the burden of having the hospital staff manually check patient records. This study focuses on the application of text classification using support vector machines and gradient tree boosting to the problem. Support vector machines and gradient tree boosting have never been applied to the problem of detecting hospital-acquired infections in Swedish patient records, and according to our experiments, they lead to encouraging results. The best result is yielded by gradient tree boosting, at 93.7 percent recall, 79.7 percent precision and 85.7 percent F1 score when using stemming. We can show that simple preprocessing techniques and parameter tuning can lead to high recall (which we aim for in screening patient records) with appropriate precision for this task.

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