Results of machine learning algorithm. by Balraj Preet Kaur (20370832)

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

The performance of classifiers based on 60 selected mRNAs. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

Subjects:

ROC comparison of machine learning algorithm. by Balraj Preet Kaur (20370832)

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

Study selection. by Sebastião Rogério da Silva Neto (11944944)

List of selected mRNAs in the feature selection step. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

Subjects:

Selected mRNAs based on association rule mining analysis, for early and late stages of PTC. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

Subjects:

Binary confusion matrix. by Chen Zhu (445894)

The maximum accuracy (lowest error rate) with the least number of ANOVA-ranked genes achieved by different feature filtering methods and classification algorithms refined by SIRRFE... by Song Cui (553627)

The performance of the SVM classifier based on 60 (30 plus 30-zeroed) selected mRNAs for GSE 60542 dataset. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Parameter settings of the comparison algorithms. by Ying Li (38224)

“…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”

Algorithm for generating hyperparameter. by Balraj Preet Kaur (20370832)

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

Feature selection process. by Balraj Preet Kaur (20370832)

“…Motivated by the above, in this proposal, we design an improved model to predict the existence of respiratory disease among patients by incorporating hyperparameter optimization and feature selection. To optimize the parameters of the machine learning algorithms, hyperparameter optimization with a genetic algorithm is proposed and to reduce the size of the feature set, feature selection is performed using binary grey wolf optimization algorithm. …”

Top mRNAs based on repeat count in early-stage and late-stage rules. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

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Twenty of the top early-stage and late-stage rules based on sorted lift value. by Seyed Mahdi Hosseiniyan Khatibi (16791475)

Subjects:

Effects of Class Imbalance and Data Scarcity on the Performance of Binary Classification Machine Learning Models Developed Based on ToxCast/Tox21 Assay Data by Changhun Kim (682542)

“…An assay matrix based on CI and DS was prepared for 335 assays with biologically intended target information, and 28 CI assays and 3 DS assays were selected. …”

An overview of the U-Net++ architecture for the area of interest selection. by Rinrada Tanthanathewin (18611171)

Comparison in terms of the selected features. by Ying Li (38224)

“…In this paper, we present an improved mountain gazelle optimizer (IMGO) based on the newly proposed mountain gazelle optimizer (MGO) and design a binary version of IMGO (BIMGO) to solve the feature selection problem for medical data. …”

The scheme of CARS-Logistic algorithm. by Maryam Sadiq (7046903)

Predictor Retention Criteria for Binary Risk Factors. Pseudo-Prospective Validation Cohort = September 2022. by Terrence J. Lee-St. John (19532684)

IRBMO vs. feature selection algorithm boxplot. by Chenyi Zhu (9383370)

“…To adapt to the feature selection problem, we convert the continuous optimization algorithm to binary form via transfer function, which further enhances the applicability of the algorithm. …”