An Improved CART Decision Tree for Datasets with Irrelevant Feature

Mahmood, Ali Mirza; Imran, Mohammed; Naganjaneyulu, Satuluri; Kuppa, Mrithyumjaya Rao; Vemulakonda, Rajesh

doi:10.1007/978-3-642-27172-4_64

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…The Random Forest Algorithm is a supervised ML technique widely used for solving regression and classification problems [90][91][92]. The algorithm is based on the idea of a forest, comprising numerous decision trees on various dataset subsets [93,94].…”

Section: Algorithmmentioning

confidence: 99%

Efficient modeling of double absorber layered structure in perovskite solar cells using machine learning techniques

Prasanna,

Goel,

Kumar

2023

Phys. Scr.

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This research article investigates the perovskite solar cells double absorber layer structure with 13 different absorber layer combinations. Our primary objective is to explore the potential for achieving high efficiency in perovskite solar cells. We present a double-layer structure for high efficiency, leveraging double absorber layers in perovskite solar cells expands spectral absorption and reduces thermalization losses, thereby enhancing power conversion efficiency and stability. Double absorber layer perovskite solar cells adaptability and integration potential further promise efficiency and reliability advancements. Through a comprehensive examination of all possible combinations of absorber layers we have identified a double absorber layer structure that exhibits an impressive efficiency exceeding 35%. To further enhance our understanding of the underlying factors influencing efficiency, we generated a dataset of 3490 samples. These samples were characterized by 20 input features and four attributes, including the efficiency of the perovskite structure. Leveraging machine learning approach, we developed model using the Random Forest algorithm to predict the efficiency and classify the structures into four distinct classes. Our findings contribute valuable insights into the optimized design of perovskite solar cells, offering a pathway towards achieving higher efficiencies and advancing the field of renewable energy.

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Section: Algorithmmentioning

confidence: 99%

Efficient modeling of double absorber layered structure in perovskite solar cells using machine learning techniques

Prasanna,

Goel,

Kumar

2023

Phys. Scr.

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“…Common machine learning techniques include the decision tree and researchers always use it to deal with classification problems [15]. The classification mechanism by decision tree is as follows: start with the root node, test an instance feature, and then distribute the instance to its child nodes based on the test results.…”

Section: Decision Treementioning

confidence: 99%

A study on the crucial indicators for breast cancer detection using machine learning algorithm

2023

J. Phys.: Conf. Ser.

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Since breast cancer is the most serious disease affecting women, early detection comes as a priority. The Wisconsin Breast Cancer Dataset (WBCD), which was retrieved from the UCI database, has been applied in numerous studies in recent years to help with the definitive diagnosis. Machine learning (ML) algorithms, such as K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM), and Neural Network (NN) can be used to attain the upshot. Although these algorithms make predictions well, advantages cannot overshadow drawbacks because the outcome is circumstantial by the peculiar dataset itself and cannot draw a direct conclusion reflecting the deeper issue. To implement ML skills to figure out the factors that influence the prediction most in a statistical dimension, this paper uses the dataset above, compares five methods, and chooses three best classifiers: KNN, RF, and SVM. After selection, the author eliminates every single variable each time to get the accuracy, and compares them with the full model’s accuracy. Having controlled variables, it can be informed that Clump Thickness and Bare Nuclei are the factors that matter most.

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“…We have employed the following five different estimation techniques to perform the experiments: classification and regression tree (CART), 20 SVM, 16 PLS, 17 KNN, 18 and RF. 19 These are estimation techniques widely used in SDEE and SFP studies (e.g., [12][13][14][15] ).…”

Section: Estimation Techniquesmentioning

confidence: 99%

An empirical comparison of validation methods for software prediction models

Ali

Gravino

2021

J Software Evolu Process

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Model validation methods (e.g., k-fold cross-validation) use historical data to predict how well an estimation technique (e.g., random forest) performs on the current (or future) data. Studies in the contexts of software development effort estimation (SDEE) and software fault prediction (SFP) have used and investigated different model validation methods. However, no conclusive indications to suggest which model validation method has a major impact on the prediction accuracy and stability of estimation techniques. Some studies have investigated model validation methods specific to data about either SDEE or SFP. To the best of our knowledge, there is no study in the literature, which has employed different validation methods both with SDEE and SFP data. The aim of this paper is to consider different methods (10) from the family of cross-validation (CV) and bootstrap validation methods to identify which one contributes to obtaining a better prediction accuracy for both types of data. We also evaluate which model validation methods allow the estimation techniques to provide stable performances (i.e., with lower variance). To this aim, we present an empirical study involving six datasets from the domain of SDEE and six datasets from the SFP domain. The results reveal that repeated 10-fold CV with SDEE and optimistic boot with SFP data are the model validation methods that provide a better prediction accuracy in a greater number of experiments than the other model validation methods. Furthermore, a model validation method can improve the prediction accuracy up to 60% with SDEE data and up to 36% when employing SFP data. The analysis also reveals that repeated fivefold CV produces more stable performances when the experiments are repeated on the same data.

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An Improved CART Decision Tree for Datasets with Irrelevant Feature

Cited by 9 publications

References 12 publications

Efficient modeling of double absorber layered structure in perovskite solar cells using machine learning techniques

Efficient modeling of double absorber layered structure in perovskite solar cells using machine learning techniques

A study on the crucial indicators for breast cancer detection using machine learning algorithm

An empirical comparison of validation methods for software prediction models

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