Development of a novel feature weighting method using CMA-ES optimization

Taşçı, Erdal; Gökalp, Osman; Uğur, Aybars

doi:10.1109/siu.2018.8404178

“…Feature selection is generally utilized for data analysis, pattern recognition, data mining, and machine learning tasks. This process aims to improve performance (e.g., tumor grading) and classification accuracy rate and provide computational efficiency by removing irrelevant or redundant features and reducing the dimensionality of data [ 12 , 13 , 14 , 15 , 16 , 17 ]. There are various feature selection methods available, such as filter methods, wrapper methods, and embedded methods [ 12 , 18 ], each with its own advantages and limitations.…”

Section: Introductionmentioning

confidence: 99%

GradWise: A Novel Application of a Rank-Based Weighted Hybrid Filter and Embedded Feature Selection Method for Glioma Grading with Clinical and Molecular Characteristics

Tasci,

Jagasia,

Zhuge

et al. 2023

Cancers

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Glioma grading plays a pivotal role in guiding treatment decisions, predicting patient outcomes, facilitating clinical trial participation and research, and tailoring treatment strategies. Current glioma grading in the clinic is based on tissue acquired at the time of resection, with tumor aggressiveness assessed from tumor morphology and molecular features. The increased emphasis on molecular characteristics as a guide for management and prognosis estimation underscores is driven by the need for accurate and standardized grading systems that integrate molecular and clinical information in the grading process and carry the expectation of the exposure of molecular markers that go beyond prognosis to increase understanding of tumor biology as a means of identifying druggable targets. In this study, we introduce a novel application (GradWise) that combines rank-based weighted hybrid filter (i.e., mRMR) and embedded (i.e., LASSO) feature selection methods to enhance the performance of feature selection and machine learning models for glioma grading using both clinical and molecular predictors. We utilized publicly available TCGA from the UCI ML Repository and CGGA datasets to identify the most effective scheme that allows for the selection of the minimum number of features with their names. Two popular feature selection methods with a rank-based weighting procedure were employed to conduct comprehensive experiments with the five supervised models. The computational results demonstrate that our proposed method achieves an accuracy rate of 87.007% with 13 features and an accuracy rate of 80.412% with five features on the TCGA and CGGA datasets, respectively. We also obtained four shared biomarkers for the glioma grading that emerged in both datasets and can be employed with transferable value to other datasets and data-based outcome analyses. These findings are a significant step toward highlighting the effectiveness of our approach by offering pioneering results with novel markers with prospects for understanding and targeting the biologic mechanisms of glioma progression to improve patient outcomes.

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“…Feature selection methods can be categorized into filter (e.g., Minimum Redundancy Maximum Relevance (mRMR)), wrapper, and embedded (e.g., Least Absolute Shrinkage and Selection Operator (LASSO)) methods, according to evaluations of feature subsets [28][29][30]. Apart from the feature selection process, feature weighting is also a crucial step in assigning suitable weights to the features for finding the most effective possible final feature subset, after applying each fold of the cross-validation operation task [26,31].…”

Section: Introductionmentioning

confidence: 99%

MGMT ProFWise: Unlocking a New Application for Combined Feature Selection and the Rank-Based Weighting Method to Link MGMT Methylation Status to Serum Protein Expression in Patients with Glioblastoma

Tasci,

Shah,

Jagasia

et al. 2024

IJMS

2

0

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Glioblastoma (GBM) is a fatal brain tumor with limited treatment options. O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation status is the central molecular biomarker linked to both the response to temozolomide, the standard chemotherapy drug employed for GBM, and to patient survival. However, MGMT status is captured on tumor tissue which, given the difficulty in acquisition, limits the use of this molecular feature for treatment monitoring. MGMT protein expression levels may offer additional insights into the mechanistic understanding of MGMT but, currently, they correlate poorly to promoter methylation. The difficulty of acquiring tumor tissue for MGMT testing drives the need for non-invasive methods to predict MGMT status. Feature selection aims to identify the most informative features to build accurate and interpretable prediction models. This study explores the new application of a combined feature selection (i.e., LASSO and mRMR) and the rank-based weighting method (i.e., MGMT ProFWise) to non-invasively link MGMT promoter methylation status and serum protein expression in patients with GBM. Our method provides promising results, reducing dimensionality (by more than 95%) when employed on two large-scale proteomic datasets (7k SomaScan® panel and CPTAC) for all our analyses. The computational results indicate that the proposed approach provides 14 shared serum biomarkers that may be helpful for diagnostic, prognostic, and/or predictive operations for GBM-related processes, given further validation.

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“…As for the combinations that contain more parameters, we utilize the method called the covariance matrix adaptation evolution strategy (CMA-ES) to achieve fast convergence. CMA-ES is commonly used for global optimization problems, such as the parameter adjustment in the neural network [42], large-scale overlapping problems [43], and feature weighting problems [44]. e objective of this work is (1) find out which single parameter or parameter combination can cause the maximal extent of uncertainty for two types of NAO events, and record their values, (2) investigate how much of uncertainties that the single parameter or parameter combination in (1) can achieve, and (3) explore the magnitude of parameter perturbations that can cause the extreme NAO events.…”

Section: Introductionmentioning

confidence: 99%

CNOP-P-Based Parameter Sensitivity Analysis for North Atlantic Oscillation in Community Earth System Model Using Intelligence Algorithms

Mu

¹

,

Li

²

,

Yuan

³

et al. 2020

Advances in Meteorology

1

0

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Model error, which results from model parameters, can cause the nonnegligible uncertainty in the North Atlantic Oscillation (NAO) simulation. Conditional nonlinear optimal perturbation related to parameter (CNOP-P) is a powerful approach to investigate the range of uncertainty caused by model parameters under a specific constraint. In this paper, we adopt intelligence algorithms to implement the CNOP-P method and conduct the sensitivity analysis of parameter combinations for NAO events in the Community Earth System Model (CESM). Among 28 model parameters of the atmospheric component, the most sensitive parameter combination for the NAO + consists of parameter for deep convection (cldfrc_dp1), minimum relative humidity for low stable clouds (cldfrc_rhminl), and the total solar irradiance (solar_const). As for the NAO − , the parameter set that can trigger the largest variation of the NAO index (NAOI) is comprised of the constant for evaporation of precip (cldwat_conke), characteristic adjustment time scale (hkconv_cmftau), and the total solar irradiance (solar_const). The most prominent uncertainties of the NAOI ( Δ NAOI ) caused by these two combinations achieve 2.12 for NAO + and −2.72 for NAO − , respectively. In comparison, the maximum level of the NAOI variation resulting from single parameters reaches 1.45 for NAO + and −1.70 for NAO − . It is indicated that the nonlinear impact of multiple parameters would be more intense than the single parameter. These results present factors that are closely related to NAO events and also provide the direction of optimizing model parameters. Moreover, the intelligence algorithms adopted in this work are proved to be adequate to explore the nonlinear interaction of parameters on the model simulation.

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Development of a novel feature weighting method using CMA-ES optimization

Cited by 5 publications

References 14 publications

GradWise: A Novel Application of a Rank-Based Weighted Hybrid Filter and Embedded Feature Selection Method for Glioma Grading with Clinical and Molecular Characteristics

GradWise: A Novel Application of a Rank-Based Weighted Hybrid Filter and Embedded Feature Selection Method for Glioma Grading with Clinical and Molecular Characteristics

MGMT ProFWise: Unlocking a New Application for Combined Feature Selection and the Rank-Based Weighting Method to Link MGMT Methylation Status to Serum Protein Expression in Patients with Glioblastoma

CNOP-P-Based Parameter Sensitivity Analysis for North Atlantic Oscillation in Community Earth System Model Using Intelligence Algorithms

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