A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification

Lima, Helen C. S. C.; Otero, Fernando E. B.; Merschmann, Luiz H. C.; Souza, Marcone Jamilson Freitas

doi:10.1109/access.2021.3112396

Cited by 12 publications

(7 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The goal is to reduce the number of features and improve classification quality, so it is optimized as a single- or multi-objective (Lima et al. 2021 ; Yan et al. 2016 ).…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

A survey on binary metaheuristic algorithms and their engineering applications

Pan

Snåšel

et al. 2022

Artif Intell Rev

View full text Add to dashboard Cite

This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.

show abstract

“…The goal is to reduce the number of features and improve classification quality, so it is optimized as a single- or multi-objective (Lima et al. 2021 ; Yan et al. 2016 ).…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

A survey on binary metaheuristic algorithms and their engineering applications

Pan

Snåšel

et al. 2022

Artif Intell Rev

View full text Add to dashboard Cite

show abstract

“…The purpose of the Pearson correlation analysis in this study was twofold: (a) identify imaging biomarkers that may improve OS prediction in glioma patients, and (b) select relevant covariates for the subsequent neural networkbased survival analysis. Feature selection was employed in order to maximize prediction performance in some ML models while minimizing computational cost [15,32]. Fig.…”

Section: Correlation Analysismentioning

confidence: 99%

“…The PMF-NN method "directly" models the survival probability function by using an FFNN with multiple output nodes [32]. For a set of m output nodes, Y(X i ) = [y 1 (X i ), y 2 (X i ), ..., y m (X i )] T , the survival probability at m different time points, [t 1 , t 2 , ..., t m ] is given by:…”

Section: Pmf-nn Survival Analysismentioning

confidence: 99%

Application of feed-forward neural network approaches to ApplicationApplication of feed-forward neural network approaches to radiomics-based survival analysis in glioma patients radiomics-based survival analysis in glioma patients

Remigio

Franich

Panettieri

2022

Preprint

View full text Add to dashboard Cite

Radiomic features extracted from MR images, along with other clinical covariates, have been shown to facilitate glioma patient prognostication via survival analysis. In this study, we apply the DeepSurv and neural network-parameterized probability mass function (PMF-NN) models to the survival analysis of glioma patients using the age and extracted radiomic features from multiparametric MRIs in the Brain Tumor Segmentation Challenge 2018 (BraTS 2018) dataset. First order and texture features were calculated for the whole tumor and peritumoral brain zone (PBZ) regions in both T1-weighted contrast-enhanced (T1-wce) and T2-weighted fluid attenuated inversion recovery (T2-FLAIR) images. To the best of our knowledge, this work is the first to apply the DeepSurv and PMF-NN models to radiomics-based survival analysis of glioma patients. Whole tumor shape features were also calculated from the delineated masks of the whole tumor. Pearson correlation analysis showed age, 4 first-order, 10 shape, and 17 texture features are correlated with the overall survival of glioma patients. These correlated features were used as input for the developed DeepSurv and PMF-NN models. The best performing DeepSurv model in our study obtained a 1050-day integrated Brier score (IBS) and C-index of 0.122 and 0.670, respectively. For the best performing PMF-NN in terms of IBS, a 1050-day IBS of 0.125 and a C-index of 0.632 were obtained. Thus, our DeepSurv and PMF-NN models provide clinically informative predictions. Our developed models outperformed the Cox proportional hazards (CPH) with supervised principal component analysis (SPCA) from a separate study in terms of IBS.

show abstract

“…Feature extraction applications pose new challenges in the selection of streaming features [2]. The feature extraction applications have several characteristics, including a) characteristics are evaluated consecutively with a set number of occurrences; and b) the trained model does not exist in advance.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In a text mining assignment for spam filtering, for example, additional features (e.g., words) are dynamically created and must therefore be exploited to filter out the spam instead of waiting for every characteristic to be collected. Traditional methodologies, which have not been developed for streaming information applications, cannot be employed in this situation since they demand that the whole extracted feature set be known beforehand to evaluate the effective attributes effectively and scientifically [2,3]. Parkinson's disease is a widespread neurological disorder.…”

Section: Literature Reviewmentioning

confidence: 99%

A Framework for Blended Sub Feature Engineering for Chronic Disease Prediction Using in-Memory Computing

Raghavendra¹,

Mahesh²,

Rao³

2022

RIA

View full text Add to dashboard Cite

Chronic diseases are among the most frequent major health concerns. Early detection of chronic illnesses can help to avoid or lessen their repercussions, potentially lowering death rates. It's an innovative technique to use machine learning algorithms to identify dangerous variables. The problem with existing feature selection procedures is that each method gives a unique collection of features that influence model validity, and current methods are incapable of performing effectively on large multidimensional datasets. We would want to present a novel model that uses a feature selection strategy to choose ideal features from large multidimensional data sets to deliver credible forecasts of chronic diseases while preserving the uniqueness of the data. To assure the success of our proposed model, we used balanced classes by applying hybrid balanced class sampling methods to the original dataset, as well as methods to provide valid data for the training model, characterization and data conversion are required. Our model was run and assessed on datasets with binary and multi-valued classifications. We utilized a variety of datasets (Parkinson's disease, arrhythmia, breast cancer, kidney disease, and diabetes). To select suitable features, the hybrid feature model is used, which includes six ensemble models and involves voting on attributes. The accuracy of the original dataset before applying the framework is recorded and compared to the accuracy of the reduced set of characteristics. The findings are given individually to allow for comparisons. We can conclude from the results that our proposed model performed best on multi-valued class datasets rather than binary class characteristics.

show abstract

A Novel Hybrid Feature Selection Algorithm for Hierarchical Classification

Cited by 12 publications

References 50 publications

A survey on binary metaheuristic algorithms and their engineering applications

A survey on binary metaheuristic algorithms and their engineering applications

Application of feed-forward neural network approaches to ApplicationApplication of feed-forward neural network approaches to radiomics-based survival analysis in glioma patients radiomics-based survival analysis in glioma patients

A Framework for Blended Sub Feature Engineering for Chronic Disease Prediction Using in-Memory Computing

Contact Info

Product

Resources

About