Min Max Normalization Based Data Perturbation Method for Privacy Protection
By Yogendra Kumar Jain. 2011
312 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Science Applications, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/57daa7d47705be3aeb09b6644417c14561da6ad5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Qi Feng
- Xiaohu Wen
- Zhi-bin He
- Jianhua Si
- Lin-shan Yang
- Zhenliang Yin
- Ravinesh C. Deo
- Manjaiah Doddaghatta Huchaiah
In Computer Science Applications
- Xiaohu Wen
- Manjaiah Doddaghatta Huchaiah
- Zhi-bin He
- Daniel Mesafint Belete
- Jianhua Si
- Ravinesh C. Deo
- Qi Feng
- Lin-shan Yang
In Sociology and Political Science
- Xiaohu Wen
- Manjaiah Doddaghatta Huchaiah
- Zhi-bin He
- Daniel Mesafint Belete
- Jianhua Si
- Ravinesh C. Deo
- Qi Feng
- Lin-shan Yang
Citations in the corpus
- Grid search in hyperparameter optimization of machine learning models for prediction of HIV/AIDS test results (2021, background): “‘n_estimators’:[10, 15, 20], ‘max_features’: ‘sqrt’,”
- Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment (2018, methodology): “In this study, all time-series river flow data were normalized using the min–max normalization method (Jain and Bhandare 2011), using the following equation: Q ¼ Q Qmin Qmax Qmin ð14Þ where Q, Q*, Qmin and Qmax are the original, transformed, minimum and maximum value of river flow, respectively.”