Preordonance correlation filter for feature selection in the high dimensional classification problem

Chamlal, Hasna; Ouaderhman, Tayeb; Aaboub, Fadwa

doi:10.1109/icds53782.2021.9626705

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…As shown in the simulation, the proposed two-step model performed well. Besides, previous published studies also have demonstrated the error cumulation issue in two-step models can be controlled well in the similar way as we did, and well not cause serious bias in the final results [ 14 , 65 – 70 ].…”

Section: Discussionsupporting

confidence: 78%

High-dimensional mediation analysis for continuous outcome with confounders using overlap weighting method in observational epigenetic study

Hu,

Chen,

Cai

et al. 2024

BMC Med Res Methodol

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Background Mediation analysis is a powerful tool to identify factors mediating the causal pathway of exposure to health outcomes. Mediation analysis has been extended to study a large number of potential mediators in high-dimensional data settings. The presence of confounding in observational studies is inevitable. Hence, it’s an essential part of high-dimensional mediation analysis (HDMA) to adjust for the potential confounders. Although the propensity score (PS) related method such as propensity score regression adjustment (PSR) and inverse probability weighting (IPW) has been proposed to tackle this problem, the characteristics with extreme propensity score distribution of the PS-based method would result in the biased estimation. Methods In this article, we integrated the overlapping weighting (OW) technique into HDMA workflow and proposed a concise and powerful high-dimensional mediation analysis procedure consisting of OW confounding adjustment, sure independence screening (SIS), de-biased Lasso penalization, and joint-significance testing underlying the mixture null distribution. We compared the proposed method with the existing method consisting of PS-based confounding adjustment, SIS, minimax concave penalty (MCP) variable selection, and classical joint-significance testing. Results Simulation studies demonstrate the proposed procedure has the best performance in mediator selection and estimation. The proposed procedure yielded the highest true positive rate, acceptable false discovery proportion level, and lower mean square error. In the empirical study based on the GSE117859 dataset in the Gene Expression Omnibus database using the proposed method, we found that smoking history may lead to the estimated natural killer (NK) cell level reduction through the mediation effect of some methylation markers, mainly including methylation sites cg13917614 in CNP gene and cg16893868 in LILRA2 gene. Conclusions The proposed method has higher power, sufficient false discovery rate control, and precise mediation effect estimation. Meanwhile, it is feasible to be implemented with the presence of confounders. Hence, our method is worth considering in HDMA studies.

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Section: Discussionsupporting

confidence: 78%

High-dimensional mediation analysis for continuous outcome with confounders using overlap weighting method in observational epigenetic study

Hu,

Chen,

Cai

et al. 2024

BMC Med Res Methodol

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“…A novel decision tree method, which is based on preordonance theory [21][22][23][24] , will be proposed in the future work.…”

Section: Discussionmentioning

confidence: 99%

Statistical analysis of various splitting criteria for decision trees

Aaboub,

Chamlal,

Ouaderhman

2023

Journal of Algorithms & Computational Technology

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Decision trees are frequently used to overcome classification problems in the fields of data mining and machine learning, owing to their many perks, including their clear and simple architecture, excellent quality, and resilience. Various decision tree algorithms are developed using a variety of attribute selection criteria, following the top-down partitioning strategy. However, their effectiveness is influenced by the choice of the splitting method. Therefore, in this work, six decision tree algorithms that are based on six different attribute evaluation metrics are gathered in order to compare their performances. The choice of the decision trees that will be compared is done based on four different categories of the splitting criteria that are criteria based on information theory, criteria based on distance, statistical-based criteria, and other splitting criteria. These approaches include iterative dichotomizer 3 (first category), C[Formula: see text] (first category), classification and regression trees (second category), Pearson’s correlation coefficient based decision tree (third category), dispersion ratio (third category), and feature weight based decision tree algorithm (last category). On eleven data sets, the six procedures are assessed in terms of classification accuracy, tree depth, leaf nodes, and tree construction time. Furthermore, the Friedman and post hoc Nemenyi tests are used to examine the results that were obtained. The results of these two tests indicate that the iterative dichotomizer 3 and classification and regression trees decision tree methods perform better than the other decision tree methodologies.

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“…Feature selection is a subset of feature extraction that involves selecting a portion of the original data to meet specific criteria for feature representation. On the other hand, feature extraction is a technique that transforms the original feature space into a different space with a distinct set of axes, reducing the data dimensionality 235 …”

Section: Machine Learning (Ml) Methods In Clinical Databasesmentioning

confidence: 99%

“…On the other hand, feature extraction is a technique that transforms the original feature space into a different space with a distinct set of axes, reducing the data dimensionality. 235 Principal Component Analysis (PCA) stands out as a prominent technique for dimensionality reduction. 236 Widely used for data dimensionality reduction and feature extraction, it effectively reduces computational and problem complexity.…”

Section: Dimensionality Reduction Algorithmsmentioning

confidence: 99%

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

Xu,

Zheng,

et al. 2023

J Evidence Based Medicine

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BackgroundIncreasingly, patient medication adherence data are being consolidated from claims databases and electronic health records (EHRs). Such databases offer an indirect avenue to gauge medication adherence in our data‐rich healthcare milieu. The surge in data accessibility, coupled with the pressing need for its conversion to actionable insights, has spotlighted data mining, with machine learning (ML) emerging as a pivotal technique. Nonadherence poses heightened health risks and escalates medical costs. This paper elucidates the synergistic interaction between medical database mining for medication adherence and the role of ML in fostering knowledge discovery.MethodsWe conducted a comprehensive review of EHR applications in the realm of medication adherence, leveraging ML techniques. We expounded on the evolution and structure of medical databases pertinent to medication adherence and harnessed both supervised and unsupervised ML paradigms to delve into adherence and its ramifications.ResultsOur study underscores the applications of medical databases and ML, encompassing both supervised and unsupervised learning, for medication adherence in clinical big data. Databases like SEER and NHANES, often underutilized due to their intricacies, have gained prominence. Employing ML to excavate patient medication logs from these databases facilitates adherence analysis. Such findings are pivotal for clinical decision‐making, risk stratification, and scholarly pursuits, aiming to elevate healthcare quality.ConclusionAdvanced data mining in the era of big data has revolutionized medication adherence research, thereby enhancing patient care. Emphasizing bespoke interventions and research could herald transformative shifts in therapeutic modalities.

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Preordonance correlation filter for feature selection in the high dimensional classification problem

Cited by 3 publications

References 8 publications

High-dimensional mediation analysis for continuous outcome with confounders using overlap weighting method in observational epigenetic study

High-dimensional mediation analysis for continuous outcome with confounders using overlap weighting method in observational epigenetic study

Statistical analysis of various splitting criteria for decision trees

Exploring patient medication adherence and data mining methods in clinical big data: A contemporary review

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