Improving k-nearest neighbor pattern recognition models for privacy-preserving data analysis

Romsaiyud, Walisa, Schnoor, Henning and Hasselbring, Wilhelm (2020) Improving k-nearest neighbor pattern recognition models for privacy-preserving data analysis. Baru, Chaitanya, Huan, Jun, Khan, Latifur, Hu, Xiaohua Tony, Ak, Ronay, Tian, Yuanyuan, Barga, Roger, Zaniolo, Carlo, Lee, Kisung and Ye, Yanfang Fanny (eds.) In 2019 IEEE International Conference on Big Data (Big Data). IEEE. pp. 5804-5813 . (doi:10.1109/BigData47090.2019.9006281).

Abstract

Supervised learning classification models use labeled data to train models on a discrete form for generating predictions. A major challenge addressed in this paper is training a machine learning model to the recognition of a pattern data perspective of the original datasets and privacy-preserving datasets to improve predictive models. The model training process, the training datasets, and validation datasets are mixed with data and privacy-preserving data cause overfitting from high variance in the machine learning algorithm. This paper addresses a k-Nearest Neighbor algorithm to build models, apply an automated hyperparameter tuning method to determine the optimal parameters based on the characteristics before the training process of a large volume datasets. Evaluating the model to achieve goals based on a high score of accuracy results on quality prediction and performance models. The experiments from our real datasets and the UCI machine learning repository show the best method for all of the training data and conduct difference experiments for improving accuracy, feasibility, correctness and reliability of the scheme.

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Contributors

Author: Wilhelm Hasselbring

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