Comparative study of regressor and classifier with decision tree using modern tools

Kushwah, Jitendra Singh; Kumar, Atul; Patel, Subhash Chandra; Soni, Rishi; Gawande, Amol; Gupta, Shyam

doi:10.1016/j.matpr.2021.11.635

Cited by 44 publications

(22 citation statements)

References 3 publications

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“…This approach will be more developed till a leaf node is identified. The decision tree's output can be the terminal node 19,28,29 . Some of the well-known decision tree induction algorithms such as CART 19 , CHAID 25 , C4.5, and C5.0 27,30 .…”

Section: Methodsmentioning

confidence: 99%

Optimization of tamoxifen solubility in carbon dioxide supercritical fluid and investigating other molecular targets using advanced artificial intelligence models

Alshahrani

Alshetaili

Alfadhel

et al. 2023

Sci Rep

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Particle size, shape and morphology can be considered as the most significant functional parameters, their effects on increasing the performance of oral solid dosage formulation are indisputable. Supercritical Carbon dioxide fluid (SCCO2) technology is an effective approach to control the above-mentioned parameters in oral solid dosage formulation. In this study, drug solubility measuring is investigated based on artificial intelligence model using carbon dioxide as a common supercritical solvent, at different pressure and temperature, 120–400 bar, 308–338 K. The results indicate that pressure has a strong effect on drug solubility. In this investigation, Decision Tree (DT), Adaptive Boosted Decision Trees (ADA-DT), and Nu-SVR regression models are used for the first time as a novel model on the available data, which have two inputs, including pressure, X1 = P(bar) and temperature, X2 = T(K). Also, output is Y = solubility. With an R-squared score, DT, ADA-DT, and Nu-SVR showed results of 0.836, 0.921, and 0.813. Also, in terms of MAE, they showed error rates of 4.30E−06, 1.95E−06, and 3.45E−06. Another metric is RMSE, in which DT, ADA-DT, and Nu-SVR showed error rates of 4.96E−06, 2.34E−06, and 5.26E−06, respectively. Due to the analysis outputs, ADA-DT selected as the best and novel model and the find optimal outputs can be shown via vector: (x1 = 309, x2 = 317.39, Y1 = 7.03e−05).

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Section: Methodsmentioning

confidence: 99%

Optimization of tamoxifen solubility in carbon dioxide supercritical fluid and investigating other molecular targets using advanced artificial intelligence models

Alshahrani

Alshetaili

Alfadhel

et al. 2023

Sci Rep

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“…Until a terminal node is found, this strategy will be tweaked and refined. The DT's forecast or outcome would be the terminal node 18 , 27 , 28 . The most useful algorithms for decision tree induction are CART 28 , CHAID 25 , C4.5, and C5.0 29 .…”

Section: Methodsmentioning

confidence: 99%

Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning

Abdelbasset

Elkholi

Ismail

et al. 2022

Sci Rep

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Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO2, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO2SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241).

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“…The Gini Index has a range of 0 to 1, with 0 denoting classification purity and 1 denoting random distribution of elements over different classes. In (Kushwah et al, 2021) this is applied to construct a decision tree using CART algorithm. The CART method generates a decision tree with the use of a binary split by using the Gini Index.…”

Section: C) Gain Ratiomentioning

confidence: 99%

A Comparative Study on Performance of Basic and Ensemble Classifiers With Various Datasets

Gunakala¹,

Shahid²

2023

acs

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Classification plays a critical role in machine learning (ML) systems for processing images, text and high -dimensional data. Predicting class labels from training data is the primary goal of classification. An optimal model for a particular classification problem is chosen on the basis of the model's performance and execution time. This paper compares and analyses the performance of basic as well as ensemble classifiers utilizing 10 -fold cross validation and also discusses their essential concepts, advantages, and disadvantages. In this study five basic classifiers namely Naïve Bayes (NB), Multi-layer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) and the ensemble of all the five classifiers along with few more combinations are compared with five University of California Irvine (UCI) ML Repository datasets and a Diabetes Health Indicators dataset from kaggle repository. To analyze and compare the performance of classifiers, evaluation metrics like Accuracy, Recall, Precision, Area Under Curve (AUC) and F-Score are used. Experimental results showed that SVM performs best on two out of the six datasets (Diabetes Health Indicators and waveform), RF performs best for Arrhythmia, Sonar, Tic-tac-toe datasets, and the best ensemble combination is found to be DT+SVM+RF on Ionosphere dataset having respective accuracies 72.58%, 90.38%, 81.63%, 73.59%, 94.78% and 94.01% and the proposed ensemble combinations outperformed over the conventional models for few datasets.

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Comparative study of regressor and classifier with decision tree using modern tools

Cited by 44 publications

References 3 publications

Optimization of tamoxifen solubility in carbon dioxide supercritical fluid and investigating other molecular targets using advanced artificial intelligence models

Optimization of tamoxifen solubility in carbon dioxide supercritical fluid and investigating other molecular targets using advanced artificial intelligence models

Development a novel robust method to enhance the solubility of Oxaprozin as nonsteroidal anti-inflammatory drug based on machine-learning

A Comparative Study on Performance of Basic and Ensemble Classifiers With Various Datasets

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