Evaluating data mining algorithms using molecular dynamics trajectories

Tatsis, Vasileios A.; Tjortjis, Christos; Tzirakis, Panagiotis

doi:10.1504/ijdmb.2013.055499

Cited by 19 publications

(5 citation statements)

References 46 publications

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“…It was reported that by applying 179 classifiers on 121 datasets from the UCI database, the RF classifier versions produced the best results in most of the cases [21]. A similar conclusion was also derived for smaller scale investigation with 65 WEKA classifies on 3 datasets [22]. In our case, the basic RF classifiers even slightly outperformed the Auto-WEKA classifier with RF in its core (98.6 vs. 97.1%).…”

Section: Resultssupporting

confidence: 85%

Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools

Bulgarevich

Tsukamoto

Kasuya

et al. 2019

Science and Technology of Advanced Materials

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It is demonstrated that optical microscopy images of steel materials could be effectively categorized into classes on preset ferrite/pearlite-, ferrite/pearlite/bainite-, and bainite/martensite-type microstructures with image pre-processing and statistical analysis including the machine learning techniques. Though several popular classifiers were able to get the reasonable class-labeling accuracy, the random forest was virtually the best choice in terms of overall performance and usability. The present categorizing classifier could assist in choosing the appropriate pattern recognition method from our library for various steel microstructures, which we have recently reported. That is, the combination of the categorizing and patternrecognizing methods provides a total solution for automatic quantification of a wide range of steel microstructures.

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Section: Resultssupporting

confidence: 85%

Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools

Bulgarevich

Tsukamoto

Kasuya

et al. 2019

Science and Technology of Advanced Materials

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“…Agreeing with information from the literature periodical, various data mining techniques are used for HDP with higher accuracy and fewer error rates [8]. Different types of studies have been conducted to target the prediction of HD, which includes the following related work: A framework for HDP named An Effective Classification Rule Technique for Heart Disease Prediction has been recommended by Vijayarani et al [9].…”

Section: Literature Reviewmentioning

confidence: 99%

Heart Disease Risk Prediction Expending of Classification Algorithms

Mary¹,

Khan²,

Asiri³

et al. 2022

Computers, Materials &Amp; Continua

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Heart disease prognosis (HDP) is a difficult undertaking that requires knowledge and expertise to predict early on. Heart failure is on the rise as a result of today's lifestyle. The healthcare business generates a vast volume of patient records, which are challenging to manage manually. When it comes to data mining and machine learning, having a huge volume of data is crucial for getting meaningful information. Several methods for predicting HD have been used by researchers over the last few decades, but the fundamental concern remains the uncertainty factor in the output data, as well as the need to decrease the error rate and enhance the accuracy of HDP assessment measures. However, in order to discover the optimal HDP solution, this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the University of California, Irvine (UCI) machine learning repository. In a comparative analysis, Mean Absolute Error (MAE), Relative Absolute Error (RAE), precision, recall, fmeasure, and accuracy are used to evaluate Linear Regression (LR), Decision Tree (J48), Naive Bayes (NB), Artificial Neural Network (ANN), Simple Cart (SC), Bagging, Decision Stump (DS), AdaBoost, Rep Tree (REPT), and Support Vector Machine (SVM). Overall, the SVM classifier surpasses other classifiers in terms of increasing accuracy and decreasing error rate, with RAE of 33.2631 and MAE of 0.165, the precision of 0.841, recall of 0.835, f-measure of 0.833, and accuracy of 83.49 percent for the dataset gathered from UCI. The SC improves accuracy and reduces the error rate for the Kaggle dataset, which is 3.30% for RAE, 0.016 percent for MAE, 0.984% for precision, 0.984 percent for recall, 0.984 percent for f-measure, and 98.44% for accuracy.

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“…In other words, the Random Committee Algorithm creates a group of base classifiers and averages their predictions. Each base classifier is based on the same data, however uses several numbers of random seed [31]. This is clear only if the base classifier is chosen at random; on the other hand, all classifiers would be the same.…”

Section: Random Committee Algorithmmentioning

confidence: 99%

Breast Cancer Prediction and Detection Using Data Mining Classification Algorithms: A Comparative Study

Keleş

2019

Teh. vjesn.

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Today, cancer has become a common disease that can afflict the life of one of every three people. Breast cancer is also one of the cancer types for which early diagnosis and detection is especially important. The earlier breast cancer is detected, the higher the chances of the patient being treated. Therefore, many early detection or prediction methods are being investigated and used in the fight against breast cancer. In this paper, the aim was to predict and detect breast cancer early with noninvasive and painless methods that use data mining algorithms. All the data mining classification algorithms in Weka were run and compared against a data set obtained from the measurements of an antenna consisting of frequency bandwidth, dielectric constant of the antenna's substrate, electric field and tumor information for breast cancer detection and prediction. Results indicate that Bagging, IBk, Random Committee, Random Forest, and SimpleCART algorithms were the most successful algorithms, with over 90% accuracy in detection. This comparative study of several classification algorithms for breast cancer diagnosis using a data set from the measurements of an antenna with a 10-fold cross-validation method provided a perspective into the data mining methods' ability of relative prediction. From data obtained in this study it can be said that if a patient has a breast cancer tumor, detection of the tumor is possible.

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Evaluating data mining algorithms using molecular dynamics trajectories

Cited by 19 publications

References 46 publications

Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools

Automatic steel labeling on certain microstructural constituents with image processing and machine learning tools

Heart Disease Risk Prediction Expending of Classification Algorithms

Breast Cancer Prediction and Detection Using Data Mining Classification Algorithms: A Comparative Study

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