RF-PCA: A New Solution for Rapid Identification of Breast Cancer Categorical Data Based on Attribute Selection and Feature Extraction

Bian, Kai; Zhou, Mengran; Hu, Feng; Lai, Wenhao

doi:10.3389/fgene.2020.566057

Cited by 18 publications

(5 citation statements)

References 45 publications

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“…Based on a previous study by Zhu et al [27], the first two PCs with 95% of the total variance could provide sufficient information to explain the difference of the sample. Generally, when the PCs have more than 85% of the total variance, it proves that the data obtained is reliable and can be used for further analysis [30], such as for discrimination or classification purposes. Other than its accuracy, PCA can visualise the cluster of the samples on a score plot and reduce the time spent on discrimination.…”

Section: Discrimination Of Herbal Products From the Zingiberaceae Fam...mentioning

confidence: 96%

Discrimination of Herbal Products from Zingiberaceae Family Using Electric Nose Combined with Chemometric Techniques

2021

MJCHEM

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Many herbs from the Zingiberaceae family are widely used as raw materials in herbal products. Some of them have a similar colour for their rhizomes and are quite hard to distinguish from each other, so it is possible to misidentify one for the other. Therefore, it is necessary to find a rapid and reliable method for discrimination of Zingiberaceae-based products. The present work investigates the possibility of applying electric nose (e-nose) combined with chemometric techniques for discrimination of herbal products from the Zingiberaceae family. Nine powdered samples consisting of Alpinia galanga, Alpinia conchigera, Curcuma longa, Curcuma zedoaria, Curcuma xanthorrhiza, Zingiber officinale, Zingiber zerumbet, Kaempferia galangal, and Kaempferia pandurata were analysed by using e-nose. As a result, e-nose identified several volatile compounds for each sample. Next, the responses from e-nose were used for principal component analysis (PCA) and discriminant function analysis (DFA) to visualise the dataset using score plots. The result demonstrates that the combination of e-nose and PCA and DFA could discriminate the samples, in which DFA gave clearer discrimination between all samples. This combination can be used for rapid discrimination of Zingiberaceae-based products, which could contribute to the quality control in the herbal industry's supply chain.

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Section: Discrimination Of Herbal Products From the Zingiberaceae Fam...mentioning

confidence: 96%

Discrimination of Herbal Products from Zingiberaceae Family Using Electric Nose Combined with Chemometric Techniques

2021

MJCHEM

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“…Receiver operating characteristic curve (ROC) and PCA method was used to visualize the prediction ability of various methods 14 . Random Forest and Principal Component Analysis methods are combined for attribute selection and accurate diagnosis of breast cancer patients 15 . Recent literatures for classifying breast cancer dataset have also been reviewed.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrative machine learning framework for classifying SEER breast cancer

Manikandan¹,

Durga²,

Ponnuraja

2023

Sci Rep

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Breast cancer is the commonest type of cancer in women worldwide and the leading cause of mortality for females. The aim of this research is to classify the alive and death status of breast cancer patients using the Surveillance, Epidemiology, and End Results dataset. Due to its capacity to handle enormous data sets systematically, machine learning and deep learning has been widely employed in biomedical research to answer diverse classification difficulties. Pre-processing the data enables its visualization and analysis for use in making important decisions. This research presents a feasible machine learning-based approach for categorizing SEER breast cancer dataset. Moreover, a two-step feature selection method based on Variance Threshold and Principal Component Analysis was employed to select the features from the SEER breast cancer dataset. After selecting the features, the classification of the breast cancer dataset is carried out using Supervised and Ensemble learning techniques such as Ada Boosting, XG Boosting, Gradient Boosting, Naive Bayes and Decision Tree. Utilizing the train-test split and k-fold cross-validation approaches, the performance of various machine learning algorithms is examined. The accuracy of Decision Tree for both train-test split and cross validation achieved as 98%. In this study, it is observed that the Decision Tree algorithm outperforms other supervised and ensemble learning approaches for the SEER Breast Cancer dataset.

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“…Unlike feature selection that selects a better subset from the original feature data set, feature extraction generates new attributes through the relationship between attributes and changes the original feature space [39]. Common methods include principal component analysis (PCA) [40] and independent component analysis (ICA) [41], linear discriminant analysis (LDA). Considering that a single type of index may be one-sided, some studies have also tried to use multi-dimensional features to complete academic predictions, but more and more studies have confirmed that when machine learning modeling, multi-feature data may bring lower prediction accuracy [42]; therefore, it is necessary to use feature extraction methods to reduce feature dimensions to improve the prediction effect and reduce the computational cost of the model [43].…”

Section: Feature Engineeringmentioning

confidence: 99%

E-Learning Performance Prediction: Mining the Feature Space of Effective Learning Behavior

Qiu

Zhu

Zhang

et al. 2022

Entropy

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Learning analysis provides a new opportunity for the development of online education, and has received extensive attention from scholars at home and abroad. How to use data and models to predict learners’ academic success or failure and give teaching feedback in a timely manner is a core problem in the field of learning analytics. At present, many scholars use key learning behaviors to improve the prediction effect by exploring the implicit relationship between learning behavior data and grades. At the same time, it is very important to explore the association between categories and prediction effects in learning behavior classification. This paper proposes a self-adaptive feature fusion strategy based on learning behavior classification, aiming to mine the effective E-learning behavior feature space and further improve the performance of the learning performance prediction model. First, a behavior classification model (E-learning Behavior Classification Model, EBC Model) based on interaction objects and learning process is constructed; second, the feature space is preliminarily reduced by entropy weight method and variance filtering method; finally, combined with EBC Model and a self-adaptive feature fusion strategy to build a learning performance predictor. The experiment uses the British Open University Learning Analysis Dataset (OULAD). Through the experimental analysis, an effective feature space is obtained, that is, the basic interactive behavior (BI) and knowledge interaction behavior (KI) of learning behavior category has the strongest correlation with learning performance.And it is proved that the self-adaptive feature fusion strategy proposed in this paper can effectively improve the performance of the learning performance predictor, and the performance index of accuracy(ACC), F1-score(F1) and kappa(K) reach 98.44%, 0.9893, 0.9600. This study constructs E-learning performance predictors and mines the effective feature space from a new perspective, and provides some auxiliary references for online learners and managers.

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RF-PCA: A New Solution for Rapid Identification of Breast Cancer Categorical Data Based on Attribute Selection and Feature Extraction

Cited by 18 publications

References 45 publications

Discrimination of Herbal Products from Zingiberaceae Family Using Electric Nose Combined with Chemometric Techniques

Discrimination of Herbal Products from Zingiberaceae Family Using Electric Nose Combined with Chemometric Techniques

An integrative machine learning framework for classifying SEER breast cancer

E-Learning Performance Prediction: Mining the Feature Space of Effective Learning Behavior

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