Privacy Preserving Feature Selection via Voted Wrapper Method for Horizontally Distributed Medical Data

2020 ◽  
pp. 145-171
Author(s):  
Yunmei Lu ◽  
Yanqing Zhang
Keyword(s):  
Feature Selection ◽  
Privacy Preserving ◽  
Medical Data ◽  
Wrapper Method

scholarly journals An improved tree model based on ensemble feature selection for classification

2019 ◽  
pp. 1290-1307 ◽  
Author(s):  
CHANDRALEKHA MOHAN ◽  
SHENBAGAVADIVU NAGARAJAN
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Ensemble Methods ◽  
Classification Performance ◽  
Medical Data ◽  
Random Trees ◽  
Machine Learning Techniques ◽  
Tree Model ◽  
Wrapper Method ◽  
Clinical Conditions

Researchers train and build specific models to classify the presence and absence of a disease and the accuracy of such classification models is continuously improved. The process of building a model and training depends on the medical data utilized. Various machine learning techniques and tools are used to handle different data with respect to disease types and their clinical conditions. Classification is the most widely used technique to classify disease and the accuracy of the classifier largely depends on the attributes. The choice of the attribute largely affects the diagnosis and performance of the classifier. Due to growing large volumes of medical data across different clinical conditions, the need for choosing relevant attributes and features still lacks method to handle datasets that target specific diseases. This study uses an ensemble-based feature selection using random trees and wrapper method to improve the classification. The proposed ensemble learning classification method derives a subset using the wrapper method, bagging, and random trees. The proposed method removes the irrelevant features and selects the optimal features for classification through probability weighting criteria. The improved algorithm has the ability to distinguish the relevant features from irrelevant features and improve the classification performance. The proposed feature selection method is evaluated using SVM, RF, and NB evaluators and the performances are compared against the FSNBb, FSSVMb, GASVMb, GANBb, and GARFb methods. The proposed method achieves mean classification accuracy of 92% and outperforms the other ensemble methods.

scholarly journals SPChain: Blockchain-based medical data sharing and privacy-preserving eHealth system

2021 ◽  
Vol 58 (4) ◽  
pp. 102604
Author(s):  
Renpeng Zou ◽  
Xixiang Lv ◽  
Jingsong Zhao
Keyword(s):  
Data Sharing ◽  
Privacy Preserving ◽  
Medical Data

Combining functional networks and sensitivity analysis as wrapper method for feature selection

2011 ◽  
Vol 38 (10) ◽  
pp. 12930-12938 ◽  
Author(s):  
Noelia Sánchez-Maroño ◽  
Amparo Alonso-Betanzos
Keyword(s):  
Sensitivity Analysis ◽  
Feature Selection ◽  
Functional Networks ◽  
Wrapper Method

scholarly journals Privacy preserving data publishing of categorical data through k ‐anonymity and feature selection

2016 ◽  
Vol 3 (1) ◽  
pp. 16-21 ◽  
Author(s):  
Aristos Aristodimou ◽  
Athos Antoniades ◽  
Constantinos S. Pattichis
Keyword(s):  
Feature Selection ◽  
Categorical Data ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Privacy Preserving Data Publishing

A HYBRID FEATURE SELECTION METHOD FOR TEXT CATEGORIZATION

2007 ◽  
Vol 15 (02) ◽  
pp. 133-151 ◽  
Author(s):  
E. MONTAÑÉS ◽  
J. R. QUEVEDO ◽  
E. F. COMBARRO ◽  
I. DÍAZ ◽  
J. RANILLA
Keyword(s):  
Feature Selection ◽  
Text Categorization ◽  
Hybrid Approach ◽  
Feature Selection Method ◽  
Selection Method ◽  
Fast Method ◽  
Evaluation Function ◽  
Wrapper Approach ◽  
Wrapper Method ◽  
Filtering Approach

Feature Selection is an important task within Text Categorization, where irrelevant or noisy features are usually present, causing a lost in the performance of the classifiers. Feature Selection in Text Categorization has usually been performed using a filtering approach based on selecting the features with highest score according to certain measures. Measures of this kind come from the Information Retrieval, Information Theory and Machine Learning fields. However, wrapper approaches are known to perform better in Feature Selection than filtering approaches, although they are time-consuming and sometimes infeasible, especially in text domains. However a wrapper that explores a reduced number of feature subsets and that uses a fast method as evaluation function could overcome these difficulties. The wrapper presented in this paper satisfies these properties. Since exploring a reduced number of subsets could result in less promising subsets, a hybrid approach, that combines the wrapper method and some scoring measures, allows to explore more promising feature subsets. A comparison among some scoring measures, the wrapper method and the hybrid approach is performed. The results reveal that the hybrid approach outperforms both the wrapper approach and the scoring measures, particularly for corpora whose features are less scattered over the categories.

A hybrid feature selection algorithm combining ReliefF and Particle swarm optimization for high-dimensional medical data

2021 ◽  
pp. 1-15
Author(s):  
Zhaozhao Xu ◽  
Derong Shen ◽  
Yue Kou ◽  
Tiezheng Nie
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Random Forest ◽  
Classification Accuracy ◽  
Particle Swarm ◽  
Medical Data ◽  
High Dimensional ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Swarm Optimization

Due to high-dimensional feature and strong correlation of features, the classification accuracy of medical data is not as good enough as expected. feature selection is a common algorithm to solve this problem, and selects effective features by reducing the dimensionality of high-dimensional data. However, traditional feature selection algorithms have the blindness of threshold setting and the search algorithms are liable to fall into a local optimal solution. Based on it, this paper proposes a hybrid feature selection algorithm combining ReliefF and Particle swarm optimization. The algorithm is mainly divided into three parts: Firstly, the ReliefF is used to calculate the feature weight, and the features are ranked by the weight. Then ranking feature is grouped according to the density equalization, where the density of features in each group is the same. Finally, the Particle Swarm Optimization algorithm is used to search the ranking feature groups, and the feature selection is performed according to a new fitness function. Experimental results show that the random forest has the highest classification accuracy on the features selected. More importantly, it has the least number of features. In addition, experimental results on 2 medical datasets show that the average accuracy of random forest reaches 90.20%, which proves that the hybrid algorithm has a certain application value.

A Perturbation Method Based on Singular Value Decomposition and Feature Selection for Privacy Preserving Data Mining

2014 ◽  
Vol 10 (1) ◽  
pp. 55-76 ◽  
Author(s):  
Mohammad Reza Keyvanpour ◽  
Somayyeh Seifi Moradi
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Singular Value Decomposition ◽  
Perturbation Method ◽  
Privacy Preserving ◽  
Singular Value ◽  
Privacy Preserving Data Mining ◽  
Selection For ◽  
Value Decomposition ◽  
Different Levels

In this study, a new model is provided for customized privacy in privacy preserving data mining in which the data owners define different levels for privacy for different features. Additionally, in order to improve perturbation methods, a method combined of singular value decomposition (SVD) and feature selection methods is defined so as to benefit from the advantages of both domains. Also, to assess the amount of distortion created by the proposed perturbation method, new distortion criteria are defined in which the amount of created distortion in the process of feature selection is considered based on the value of privacy in each feature. Different tests and results analysis show that offered method based on this model compared to previous approaches, caused the improved privacy, accuracy of mining results and efficiency of privacy preserving data mining systems.

K-Anonymous Privacy Preserving Scheme Based on Bilinear Pairings over Medical Data

2020 ◽  
pp. 381-393
Author(s):  
Linghang Meng ◽  
Xueshu Hong ◽  
Yingwen Chen ◽  
Yuke Ding ◽  
Chengzhuo Zhang
Keyword(s):  
Bilinear Pairings ◽  
Privacy Preserving ◽  
Medical Data
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