Comparative Study of Principal Component Analysis (PCA) based on Decision Tree Algorithms

Oo, Aung Nway

doi:10.31695/ijasre.2018.32767

Cited by 3 publications

(6 citation statements)

References 7 publications

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“…In classifying and predict the target variables' preset classes, classi cation is a typical machine learning approach. In this study, we looked at a number of machine learning classi ers and chose ve cutting-edge techniques that are often used to forecast academic success [3][4][5][6][7][8][9][10][11][12][13][14]. This study proposes a hybrid approach known as arbitrator miniature that combines factor analysis with the following ve machine learning techniques: Support Vector Machine, Adaboost, Gradient Boost, Random Forest, and Logistic Regression.…”

Section: Baseline Modelsmentioning

confidence: 99%

Arbitrator Miniature: A Paradigm using Data Science Methods to Predict Academic Performance

Malik¹,

jothimani²

2022

Preprint

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Informational tools are necessary at schools and colleges due to the sheer volume and diversity of data they handle. Numerous scholars has emphasized towards applying machine learning to retrieve information from the education database to enable students and educators in attaining greater results as a means of simplifying essential work. Selecting efficient tactics that might produce acceptable prediction performance is a challenging task for prediction models. In order to improve classification performance by addressing the misclassification issue, this study proposes a hybrid approach known as arbitrator miniature that combines factor analysis with the following nine machine learning techniques: Support vector machine, Random Forest, K Nearest Neighbor, Logistic Regression, Artificial neural network, Decision Tree, XG boost, Ada boost and Naïve Bayes. To evaluate the robustness of the suggested models, student datasets from a variety of academic fields at diploma-granting institutions in Karnataka, India, were used. In order to assess the proposed model using the datasets, assessment criteria such as classification accuracy and root mean square error were employed. This study’s findings revealed that proposed arbitrator miniature model might significantly improve classification performance. For the purpose of resolving prediction and classification issues, the proposed arbitrator miniature may be viewed as the best prediction models.

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Section: Baseline Modelsmentioning

confidence: 99%

Arbitrator Miniature: A Paradigm using Data Science Methods to Predict Academic Performance

Malik¹,

jothimani²

2022

Preprint

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“…(ii) The RF algorithms generated the highest accuracy. [12] (i) J48, CART, and RF classifiers were proposed with principal component analysis (PCA). (ii) PCA-RF was found to generate the highest accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…Classification is a common technique in machine learning that was used in order to classify and predict the categories or predefined classes of target variables. In this work, we observed several machine learning classifiers and selected the four state-of-the-art methods which are popularly used in predicting academic performances [3][4][5][6][7][8][9][10][11][12][13][14]. The four proposed algorithms are support vector machine, naïve Bayes C5.0 of the decision tree, and random forest.…”

Section: A the Baseline Modelsmentioning

confidence: 99%

“…As in the name indicates its meaning, the random forest is an algorithm builds the forest with a number of trees. A random forest algorithm is a treebased tool that grows many classification trees [12]. It is a kind of ensemble classifier that combines several classification trees to create a new classifier.…”

Section: ) Random Forest (Rf)mentioning

confidence: 99%

“…However, the challenging task is to find the optimal algorithm which could produce satisfying results. Machine learning algorithms such as naïve Bayes, logistic regression, artificial neural networks, decision tree, random forest, support vector machine, k-nearest neighbor, and more, were popularly used to analyze and predict academic performance [3][4][5][6][7][8][9][10][11][12][13][14]. The performance of each model is varied from dataset to dataset, which relies on the characteristics and quality of data.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Machine Learning Algorithms for Predicting Academic Performance

Sokkhey¹,

Okazaki²

2020

IJACSA

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The large volume of data and its complexity in educational institutions require the sakes from informative technologies. In order to facilitate this task, many researchers have focused on using machine learning to extract knowledge from the education database to support students and instructors in getting better performance. In prediction models, the challenging task is to choose the effective techniques which could produce satisfying predictive accuracy. Hence, in this work, we introduced a hybrid approach of principal component analysis (PCA) as conjunction with four machines learning (ML) algorithms: random forest (RF), C5.0 of decision tree (DT), and naïve Bayes (NB) of Bayes network and support vector machine (SVM), to improve the performances of classification by solving the misclassification problem. Three datasets were used to confirm the robustness of the proposed models. Through the given datasets, we evaluated the classification accuracy and root mean square error (RSME) as evaluation metrics of the proposed models. In this classification problem, 10-fold cross-validation was proposed to evaluate the predictive performance. The proposed hybrid models produced very prediction results which shown itself as the optimal prediction and classification algorithms.

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A Better Decision Tree: The Max-Cut Decision Tree with Modified PCA Improves Accuracy and Running Time

Bodine

2022

SN COMPUT. SCI.

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Decision trees are a widely used method for classification, both alone and as the building blocks of multiple different ensemble learning methods. The Max Cut decision tree introduced here involves novel modifications to a standard, baseline variant of a classification decision tree, CART Gini. One modification involves an alternative splitting metric, Max Cut, based on maximizing the distance between all pairs of observations that belong to separate classes and separate sides of the threshold value. The other modification, Node Means PCA, selects the decision feature from a linear combination of the input features constructed using an adjustment to principal component analysis (PCA) locally at each node. Our experiments show that this node-based, localized PCA with the Max Cut splitting metric can dramatically improve classification accuracy while also significantly decreasing computational time compared to the CART Gini decision tree. These improvements are most significant for higher-dimensional datasets. For the example dataset CIFAR-100, the modifications enabled a 49% improvement in accuracy, relative to CART Gini, while providing a $$6.8 \times$$ 6.8 × speed up compared to the Scikit-Learn implementation of CART Gini. These introduced modifications are expected to dramatically advance the capabilities of decision trees for difficult classification tasks.

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Comparative Study of Principal Component Analysis (PCA) based on Decision Tree Algorithms

Cited by 3 publications

References 7 publications

Arbitrator Miniature: A Paradigm using Data Science Methods to Predict Academic Performance

Arbitrator Miniature: A Paradigm using Data Science Methods to Predict Academic Performance

Hybrid Machine Learning Algorithms for Predicting Academic Performance

A Better Decision Tree: The Max-Cut Decision Tree with Modified PCA Improves Accuracy and Running Time

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