Joint model for feature selection and parameter optimization coupled with classifier ensemble in chemical mention recognition

Ekbal, Asif; Saha, Sriparna

doi:10.1016/j.knosys.2015.04.015

Cited by 23 publications

(15 citation statements)

References 21 publications

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“…Recently, artificial intelligence (AI) methodologies can be used to improve the available classification model. At the same time, the existence of several features in the high-dimensional medical data resulted in different problems such as overfitting, high computation complexity and low interoperability of the finishing model 7,8 . The easiest method to resolve the problem is to decrease the number of features using Feature selection (FS) approach.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease

Elhoseny

Shankar

Uthayakumar

2019

Sci Rep

149

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At present times, healthcare systems are updated with advanced capabilities like machine learning (ML), data mining and artificial intelligence to offer human with more intelligent and expert healthcare services. This paper introduces an intelligent prediction and classification system for healthcare, namely Density based Feature Selection (DFS) with Ant Colony based Optimization (D-ACO) algorithm for chronic kidney disease (CKD). The proposed intelligent system eliminates irrelevant or redundant features by DFS in prior to the ACO based classifier construction. The proposed D-ACO framework three phases namely preprocessing, Feature Selection (FS) and classification. Furthermore, the D-ACO algorithm is tested using benchmark CKD dataset and the performance are investigated based on different evaluation factors. Comparing the D-ACO algorithm with existing methods, the presented intelligent system outperformed the other methodologies with a significant improvisation in classification accuracy using fewer features.

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Section: Introductionmentioning

confidence: 99%

Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease

Elhoseny

Shankar

Uthayakumar

2019

Sci Rep

149

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“…Another important difference is the preservation of the best solutions, through an elitist selection according to the fitness and spread of solutions. Ekbal and Saha (2015) applied NSGA-II to jointly optimize hyperparameters and features, and demonstrated the superiority of the resulting models over others constructed with all the features and default hyperparameters. Binder et al (2020) observed analogous results optimizing a SVM, kkNN, and XGBoost.…”

Section: Metaheuristic-based Hpo Algorithmsmentioning

confidence: 99%

“…The algorithm proposed by Martinez-de Pison et al (2017) combines HPO with feature selection. Unlike other algorithms (Ekbal & Saha, 2015;J. Guo et al, 2019;León, Ortega, & Ortiz, 2019), the proposed method considers feature selection combined with HPO.…”

Section: Hybrid Hpo Algorithmsmentioning

confidence: 99%

A survey on multi-objective hyperparameter optimization algorithms for Machine Learning

Morales-Hernández¹,

Nieuwenhuyse²,

Rojas³

2021

Preprint

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Hyperparameter optimization (HPO) is a necessary step to ensure the best possible performance of Machine Learning (ML) algorithms. Several methods have been developed to perform HPO; most of these are focused on optimizing one performance measure (usually an error-based measure), and the literature on such single-objective HPO problems is vast. Recently, though, algorithms have appeared which focus on optimizing multiple conflicting objectives simultaneously. This article presents a systematic survey of the literature published between 2014 and 2020 on multi-objective HPO algorithms, distinguishing between metaheuristicbased algorithms, metamodel-based algorithms and approaches using a mixture of both. We also discuss the quality metrics used to compare multi-objective HPO procedures, and present future research directions.

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“…Therefore, wrapper methods usually have a better learning effect than filter methods [ 33 , 34 ]. Commonly used wrapper methods include genetic algorithms (GAs) [ 35 ] and particle swarm optimization (PSO) [ 36 ].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Machine Learning-Based Model for Predicting Compressive Strength of Ready-Mix Concrete

Zhou

et al. 2021

Materials

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Considering that compressive strength (CS) is an important mechanical property parameter in many design codes, in order to ensure structural safety, concrete CS needs to be tested before application. However, conducting CS tests with multiple influencing variables is costly and time-consuming. To address this issue, a machine learning-based modeling framework is put forward in this work to evaluate the concrete CS under complex conditions. The influential factors of this process are systematically categorized into five aspects: man, machine, material, method and environment (4M1E). A genetic algorithm (GA) was applied to identify the most important influential factors for CS modeling, after which, random forest (RF) was adopted as the modeling algorithm to predict the CS from the selected influential factors. The effectiveness of the proposed model was tested on a case study, and the high Pearson correlation coefficient (0.9821) and the low mean absolute percentage error and delta (0.0394 and 0.395, respectively) indicate that the proposed model can deliver accurate and reliable results.

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Joint model for feature selection and parameter optimization coupled with classifier ensemble in chemical mention recognition

Cited by 23 publications

References 21 publications

Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease

Intelligent Diagnostic Prediction and Classification System for Chronic Kidney Disease

A survey on multi-objective hyperparameter optimization algorithms for Machine Learning

Comprehensive Machine Learning-Based Model for Predicting Compressive Strength of Ready-Mix Concrete

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