Cloud platform operating data preprocessing method based on I-GMM algorithm

Li, Weiliang; Zhang, Xiaoliang; Dong, Yaozhong; Liu, Yuecan; Chao, Kai Chon

doi:10.1109/iaeac50856.2021.9390724

Cited by 1 publication

(1 citation statement)

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“…An actor-critic model is developed to optimize the weights in the deep reinforcement learning based ensemble learning framework [25]. Gaussian mixture model (GMM) based data preprocessing method is used for a power grid environment algorithm [26]. Data preprocessing to handle imbalance data for the industrial scenario using a time series prediction is dealt in [27].…”

Section: Introductionmentioning

confidence: 99%

Early disease prediction algorithm for hypertension-based diseases using data aware algorithms

Shaikh

Parvati

Biradar

2022

IJEECS

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This paper <span lang="EN-US">implements a data aware early prediction of hypertension-based diseases. Automated data preprocessing method that adopts for both balanced and unbalanced data is the data aware method included in the disease classification algorithm. Proposed data aware data preprocessing method is evaluated on the ensemble learning based classification algorithm for early disease prediction. Data aware preprocessing method adopts isolation forest algorithm for outlier detection as part of the automation. Automated sampling method of applying the sampling corresponding to either balanced or unbalanced data is adopted. Performance evaluation of the proposed data aware algorithm using isolation forest algorithm for anomaly detection is experimented. Python based implementation of the proposed data aware classification algorithm inferred a better area under the curve (AUC) receiver operating characteristics (ROC) curve for isolation forest implementation in data preprocessing automation thus developed. While the individual classifiers multilayer perceptron classifier approached till 0.918 (AUC) in the ROC-AUC curve. The ensemble learning algorithm that included multilayer perceptron classifier, logistic regression classifier, support vector classifier and decision tree algorithm with the isolation forest-based anomaly detection algorithm performed better than the individual machine learning algorithm with 0.922 (AUC) in the ROC-AUC curve.</span>

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Section: Introductionmentioning

confidence: 99%