Partial sufficient variable screening with categorical controls

Ke, Chenlu; Wei, Yang; Yuan, Qingcong; Lu, Li

doi:10.1016/j.csda.2023.107784

Computational Statistics & Data Analysis

2023

DOI: 10.1016/j.csda.2023.107784

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Partial sufficient variable screening with categorical controls

Chenlu Ke

Yang Wei

Qingcong Yuan

et al.

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2024

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“…Alternative data analysis techniques, such as Bayesian algorithms, offer solutions with advantages like handling parametric uncertainty in the study of irregular models [18]. In regression analysis involving predictors, variables are traced from the idea of sufficiency through subdivisions identified by categorical variables [19].…”

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confidence: 99%

A Clustering and PL/SQL-Based Method for Assessing MLP-Kmeans Modeling

Silva-Blancas,

Jiménez-Hernández,

Herrera-Navarro

et al. 2024

Computers

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With new high-performance server technology in data centers and bunkers, optimizing search engines to process time and resource consumption efficiently is necessary. The database query system, upheld by the standard SQL language, has maintained the same functional design since the advent of PL/SQL. This situation is caused by recent research focused on computer resource management, encryption, and security rather than improving data mining based on AI tools, machine learning (ML), and artificial neural networks (ANNs). This work presents a projected methodology integrating a multilayer perceptron (MLP) with Kmeans. This methodology is compared with traditional PL/SQL tools and aims to improve the database response time while outlining future advantages for ML and Kmeans in data processing. We propose a new corollary: hk→H=SSE(C),wherek>0and∃X, executed on application software querying data collections with more than 306 thousand records. This study produced a comparative table between PL/SQL and MLP-Kmeans based on three hypotheses: line query, group query, and total query. The results show that line query increased to 9 ms, group query increased from 88 to 2460 ms, and total query from 13 to 279 ms. Testing one methodology against the other not only shows the incremental fatigue and time consumption that training brings to database query but also that the complexity of the use of a neural network is capable of producing more precision results than the simple use of PL/SQL instructions, and this will be more important in the future for domain-specific problems.

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confidence: 99%

A Clustering and PL/SQL-Based Method for Assessing MLP-Kmeans Modeling

Silva-Blancas,

Jiménez-Hernández,

Herrera-Navarro

et al. 2024

Computers

View full text Add to dashboard Cite

show abstract

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Partial sufficient variable screening with categorical controls

Cited by 1 publication

References 34 publications

A Clustering and PL/SQL-Based Method for Assessing MLP-Kmeans Modeling

A Clustering and PL/SQL-Based Method for Assessing MLP-Kmeans Modeling

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