针对PHM(Prognostic and Health Management)中数据挖掘和知识获取困难的问题,提出一种以J48决策树算法为基础的故障诊断方法。采用了开源数据挖掘软件Weka,对CTSV滤波器故障仿真数据进行计算,对故障数据进行属性清理和参数选择。生成...针对PHM(Prognostic and Health Management)中数据挖掘和知识获取困难的问题,提出一种以J48决策树算法为基础的故障诊断方法。采用了开源数据挖掘软件Weka,对CTSV滤波器故障仿真数据进行计算,对故障数据进行属性清理和参数选择。生成的决策树模型有很高的交叉验证率和分类效果。展开更多
Tuberculosis remains an important problem in public health that threatens the world, including the Philippines. Treatment relapse continues to place a severe problem on patients and TB programs worldwide. A significan...Tuberculosis remains an important problem in public health that threatens the world, including the Philippines. Treatment relapse continues to place a severe problem on patients and TB programs worldwide. A significant reason for the development of decline is poor compliance with medical treatments. The objectives of this research are to generate a predictive data mining model to classify the treatment relapse of TB patients and to identify the features influencing the category of treatment relapse. The TB patient dataset is applied and tested in decision tree J48 algorithm using WEKA. The J48 model identified the three (3) significant independent variables (DSSM Result, Age, and Sex) as predictors of category treatment relapse.展开更多
The inherently unique qualities of the heart infer the candidacy for the domain of biometrics, which applies physiological attributes to establish the recognition of a person’s identity. The heart’s characteristics ...The inherently unique qualities of the heart infer the candidacy for the domain of biometrics, which applies physiological attributes to establish the recognition of a person’s identity. The heart’s characteristics can be ascertained by recording the electrical signal activity of the heart through the acquisition of an electrocardiogram (ECG). With the application of machine learning the subject specific ECG signal can be differentiated. However, the process of distinguishing subjects through machine learning may be considered esoteric, especially for contributing subject matter experts external to the domain of machine learning. A resolution to this dilemma is the application of the J48 decision tree available through the Waikato Environment for Knowledge Analysis (WEKA). The J48 decision tree elucidates the machine learning process through a visualized decision tree that attains classification accuracy through the application of thresholds applied to the numeric attributes of the feature set. Additionally, the numeric attributes of the feature set for the application of the J48 decision tree are derived from the temporal organization of the ECG signal maxima and minima for the respective P, Q, R, S, and T waves. The J48 decision tree achieves considerable classification accuracy for the distinction of subjects based on their ECG signal, for which the machine learning model is briskly composed.展开更多
Software programs are always prone to change for several reasons. In a software product line, the change is more often as many software units are carried from one release to another. Also, other new files are added to...Software programs are always prone to change for several reasons. In a software product line, the change is more often as many software units are carried from one release to another. Also, other new files are added to the reused files. In this work, we explore the possibility of building a model that can predict files with a high chance of experiencing the change from one release to another. Knowing the files that are likely to face a change is vital because it will help to improve the planning, managing resources, and reducing the cost. This also helps to improve the software process, which should lead to better software quality. Also, we explore how different learners perform in this context, and if the learning improves as the software evolved. Predicting change from a release to the next release was successful using logistic regression, J48, and random forest with accuracy and precision scored between 72% to 100%, recall scored between 74% to 100%, and F-score scored between 80% to 100%. We also found that there was no clear evidence regarding if the prediction performance will ever improve as the project evolved.展开更多
文摘针对PHM(Prognostic and Health Management)中数据挖掘和知识获取困难的问题,提出一种以J48决策树算法为基础的故障诊断方法。采用了开源数据挖掘软件Weka,对CTSV滤波器故障仿真数据进行计算,对故障数据进行属性清理和参数选择。生成的决策树模型有很高的交叉验证率和分类效果。
文摘Tuberculosis remains an important problem in public health that threatens the world, including the Philippines. Treatment relapse continues to place a severe problem on patients and TB programs worldwide. A significant reason for the development of decline is poor compliance with medical treatments. The objectives of this research are to generate a predictive data mining model to classify the treatment relapse of TB patients and to identify the features influencing the category of treatment relapse. The TB patient dataset is applied and tested in decision tree J48 algorithm using WEKA. The J48 model identified the three (3) significant independent variables (DSSM Result, Age, and Sex) as predictors of category treatment relapse.
文摘The inherently unique qualities of the heart infer the candidacy for the domain of biometrics, which applies physiological attributes to establish the recognition of a person’s identity. The heart’s characteristics can be ascertained by recording the electrical signal activity of the heart through the acquisition of an electrocardiogram (ECG). With the application of machine learning the subject specific ECG signal can be differentiated. However, the process of distinguishing subjects through machine learning may be considered esoteric, especially for contributing subject matter experts external to the domain of machine learning. A resolution to this dilemma is the application of the J48 decision tree available through the Waikato Environment for Knowledge Analysis (WEKA). The J48 decision tree elucidates the machine learning process through a visualized decision tree that attains classification accuracy through the application of thresholds applied to the numeric attributes of the feature set. Additionally, the numeric attributes of the feature set for the application of the J48 decision tree are derived from the temporal organization of the ECG signal maxima and minima for the respective P, Q, R, S, and T waves. The J48 decision tree achieves considerable classification accuracy for the distinction of subjects based on their ECG signal, for which the machine learning model is briskly composed.
文摘Software programs are always prone to change for several reasons. In a software product line, the change is more often as many software units are carried from one release to another. Also, other new files are added to the reused files. In this work, we explore the possibility of building a model that can predict files with a high chance of experiencing the change from one release to another. Knowing the files that are likely to face a change is vital because it will help to improve the planning, managing resources, and reducing the cost. This also helps to improve the software process, which should lead to better software quality. Also, we explore how different learners perform in this context, and if the learning improves as the software evolved. Predicting change from a release to the next release was successful using logistic regression, J48, and random forest with accuracy and precision scored between 72% to 100%, recall scored between 74% to 100%, and F-score scored between 80% to 100%. We also found that there was no clear evidence regarding if the prediction performance will ever improve as the project evolved.
