Selecting critical features for data classification based on machine learning methods

Chen, Rung-Ching; Dewi, Christine; Huang, Su-Wen; Caraka, Rezzy Eko

doi:10.1186/s40537-020-00327-4

Cited by 435 publications

(240 citation statements)

References 97 publications

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“…Table 5 explains the best model classifier is SVM-DHGLM with 6 features of importance and accuracy was 0.966. In the previous research [4], the best model was RF+RF with an accuracy of 0.9336. After we added the DHGLM accuracy was 0.966.…”

Section: B Employing In Uci Machine Learning Repository Datasetmentioning

confidence: 91%

“…Third, Human Activity Recognition Using Smartphones Dataset. For more detail, see [4]. Table 3 and Table 4 uses seven predictors and two classes (No and Yes) with 36170 samples.…”

Section: B Employing In Uci Machine Learning Repository Datasetmentioning

confidence: 99%

“…Long story short, the same instrument by using the Car Evaluation Dataset. In previous study [4] using the resampling cross-validation setting of 10-fold and the tuning parameter "sigma" was held constantly at a value of 0.07348688, C = 0.5 reach the accuracy 0.8346161, and kappa 0.6319634. For this cases, we run the different step by employing double HGLM, the construction step can be seen in the appendix.…”

Section: B Employing In Uci Machine Learning Repository Datasetmentioning

confidence: 99%

“…Data mining required to learn processes are frequently divided into two main types, respectively supervised and unsupervised [3]. During the first, unsupervised learning approach, the learning method is applied without any training stage without any target or learner, throughout this case the data label [4].…”

Section: Introductionmentioning

confidence: 99%

“…There are already several learning algorithms that have already been established, which include K-Nearest Neighbor [5], Artificial Neural Network [6], Naïve Bayes [7], Support Vector Machine [8] and many more [9]. This algorithm has strengths and weaknesses [4]. However, all implementations have had the same concept, in other words learning because then, at the end of the training, the model can clearly identify -input data mostly on output class label.…”

Section: Introductionmentioning

confidence: 99%

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Using Hierarchical Likelihood Towards Support Vector Machine: Theory and Its Application

et al. 2020

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The H-likelihood method proposed by Lee and Nelder (1996) is extensively used in a wide range of data. In terms of direction, repetitive measured data within classification can be examined employing hierarchical generalized linear models (HGLMs). Whether we are concerned in multiple endpoints which are correlated, instead Multivariate Double Hierarchical Generalized Linear Models (DHGLM) can be taken into consideration. This paper addresses the implementation of this principle to vector selection and support machines. Based on the analysis with the fish morphology class Sardinella lemuru (Bali sardinella) and setting the best epsilon 0.7 cost 4 parameter reaching best performance: 0.2327401. Predictive value of fish sex was calculated 0.997319 and Region under the curve: 0.8967. At the same time, we extend the large-scale case studies for stress testing of the SVM method by using three datasets from UCI machine learning repository including the Bank marketing dataset, the car evaluation database and Human Activity Recognition Using Smartphones dataset. In a nutshell by employing SVM-DHGLM increased the accuracy, precision, recall, for feature selection and classification. Long story short, the H-likelihood provides an excellent and usable structure for statistical inference of the unobservable general deterministic model, while preserving the advantages of the original probability structure for fixed parameters. We presume that more new groups of models will be created and that the H-likelihood will be commonly used for their inferences and the application in big data and machine learning.

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Section: B Employing In Uci Machine Learning Repository Datasetmentioning

confidence: 91%

“…Third, Human Activity Recognition Using Smartphones Dataset. For more detail, see [4]. Table 3 and Table 4 uses seven predictors and two classes (No and Yes) with 36170 samples.…”

Section: B Employing In Uci Machine Learning Repository Datasetmentioning

confidence: 99%

Section: B Employing In Uci Machine Learning Repository Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Hierarchical Likelihood Towards Support Vector Machine: Theory and Its Application

et al. 2020

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Feature Prioritization and Selection Methods

2022

Automation and Computational Intelligence for Road Maintenance and Management

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Classification of cardiac differentiation outcome, percentage of cardiomyocytes on day 10 of differentiation, for hydrogel‐encapsulated hiPSCs

Mohammadi

Hashemi

Finklea

et al. 2022

J Adv Manuf & Process

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This study employed machine learning (ML) models to predict the cardiomyocyte (CM) content following differentiation of human induced pluripotent stem cells (hiPSCs) encapsulated in hydrogel microspheroids and to identify the main experimental variables affecting the CM yield. Understanding how to enhance CM generation using hiPSCs is critical in moving toward large‐scale production and implementing their use in developing therapeutic drugs and regenerative treatments. Cardiomyocyte production has entered a new era with improvements in the differentiation process. However, existing processes are not sufficiently robust for reliable CM manufacturing. Using ML techniques to correlate the initial, experimentally specified stem cell microenvironment's impact on cardiac differentiation could identify important process features. The initial tunable (controlled) input features for training ML models were extracted from 85 individual experiments. Subsets of the controlled input features were selected using feature selection and used for model construction. Random forests, Gaussian process, and support vector machines were employed as the ML models. The models were built to predict two classes of sufficient and insufficient for CM content on differentiation day 10. The best model predicted the sufficient class with an accuracy of 75% and a precision of 71%. The identified key features including post‐freeze passage number, media type, PF fibrinogen concentration, CHIR/S/V, axial ratio, and cell concentration provided insight into the significant experimental conditions. This study showed that we can extract information from the experiments and build predictive models that could enhance the cell production process by using ML techniques.

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Selecting critical features for data classification based on machine learning methods

Cited by 435 publications

References 97 publications

Using Hierarchical Likelihood Towards Support Vector Machine: Theory and Its Application

Using Hierarchical Likelihood Towards Support Vector Machine: Theory and Its Application

Feature Prioritization and Selection Methods

Classification of cardiac differentiation outcome, percentage of cardiomyocytes on day 10 of differentiation, for hydrogel‐encapsulated hiPSCs

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