Weighting Approaches in Data Mining and Knowledge Discovery: A Review

Hajirahimi, Zahra; Khashei, Mehdi

doi:10.1007/s11063-023-11332-y

Cited by 3 publications

(2 citation statements)

References 361 publications

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“…The advancement of technology has facilitated the accumulation of vast amounts of data from various sources such as databases, web repositories, and files, necessitating robust tools for analysis and decision-making 1 , 2 . Data mining, employing techniques such as support vector machine (SVM), decision trees, neural networks, clustering, fuzzy logic, and genetic algorithms, plays a pivotal role in extracting information and uncovering hidden patterns within the data 3 , 4 .…”

Section: Introductionmentioning

confidence: 99%

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis

Raza,

Jamal,

Qureshi

et al. 2024

Sci Rep

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Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson’s patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson’s dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson’s disease analysis.

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Section: Introductionmentioning

confidence: 99%

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis

Raza,

Jamal,

Qureshi

et al. 2024

Sci Rep

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show abstract

“…Farfán and Cea [27] built a hydrological ensemble model based on artificial neural networks, enhancing the model results in terms of linear correlation, bias, and variability. The models mentioned above allocate weights based on the individual performance of a single model throughout the entire training phase [28]. However, in the context of runoff forecasting, the data characteristics of model predictions at different time points may exhibit varying correlations with training data features [29].…”

Section: Introductionmentioning

confidence: 99%

Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction

Xie,

Chen,

et al. 2023

Water

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Hydrological forecasting plays a crucial role in mitigating flood risks and managing water resources. Data-driven hydrological models demonstrate exceptional fitting capabilities and adaptability. Recognizing the limitations of single-model forecasting, this study introduces an innovative approach known as the Improved K-Nearest Neighbor Multi-Model Ensemble (IKNN-MME) method to enhance the runoff prediction. IKNN-MME dynamically adjusts model weights based on the similarity of historical data, acknowledging the influence of different training data features on localized predictions. By combining an enhanced K-Nearest Neighbor (KNN) algorithm with adaptive weighting, it offers a more powerful and flexible ensemble. This study evaluates the performance of the IKNN-MME method across four basins in the United States and compares it to other multi-model ensemble methods and benchmark models. The results underscore its outstanding performance and adaptability, offering a promising avenue for improving runoff forecasting.

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Data‐Driven Detection of Integrated Energy Theft Based on VMD‐MIC and Informer

Liu,

Wang,

Liu

et al. 2024

Advcd Theory and Sims

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Integrated energy systems (IES) may experience energy theft through disinformation injection attacks. With electricity as the primary target, the attacks are essentially two way. They gain additional benefits by manipulating meter readings on both the consumer and supply side. Regression based deep learning models with feature engineering can be used to detect these attacks. To detect energy theft more efficiently, this paper proposes an integrated energy systems theft detection (IESTD) based on variational modal decomposition‐maximal information coefficient (VMD‐MIC) and Informer. And using kernel density estimation (KDE), the error between the predicted and true values is fitted as a probability density function (PDF).The thresholds for the confidence intervals of the PDF and the thresholds for the number of outliers are set as the detection metrics. Simulations are performed on the three generated datasets. The results show that the model has good detection accuracy and strong robustness.

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Weighting Approaches in Data Mining and Knowledge Discovery: A Review

Cited by 3 publications

References 361 publications

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis

Adaptive neighborhood rough set model for hybrid data processing: a case study on Parkinson’s disease behavioral analysis

Application of the Improved K-Nearest Neighbor-Based Multi-Model Ensemble Method for Runoff Prediction

Data‐Driven Detection of Integrated Energy Theft Based on VMD‐MIC and Informer

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