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decision tree regression Analysis of Proposed Metric Suite for Software Fault Prediction

SN Computer Science 2024年第1期5卷 69页

作者： Kaur, Gurmeet Pruthi, Jyoti Gandhi, Parul MRU Faridabad India CST MRU Faridabad India FCA MRIIRS Faridabad India

The objective of this study is to identify a metric suite for software fault prediction that can solve challenges related to reliability, quality, time, and cost of the software project. This research paper describes the design and analysis of the proposed metric suite (requirement-based metric and agile-based metric) to predict faults in software projects that is based on a formula using process metrics as inputs from a real-time, wider dataset from the PROMISE repository. The formula has been developed in Python. Pearson correlation analysis has been applied to determine the relationship between the proposed metric suite and the faults (dependent variable) of software projects. The correlation coefficients of requirement-based metrics and agile-based metrics with faults are 0.82165 and 0.731033, respectively, which implies that the proposed metric suite has a strong positive relationship with the faults. For empirical analysis, decision-tree regression of machine learning has been used to explore the validity of the proposed metric suite. The regression analysis outcomes based on the statistical performance evolution measures (AUC and F1-measure) of the requirement-based metric with predicted faults are 0.9871 and 0.9367, respectively, and of the agile-based metric with predicted faults are 1.0 and 0.67, respectively, revealing that the proposed metric suite can be applied as the best predictor for the prediction of faults. By comparing the prediction and validation results of the proposed metrics for the same dataset, it has been concluded that the requirement-based metric has the best performance with values of MMRE, RMSE, and NRMSE of 3.61e-05, 6.07779, and 0.0034748, respectively. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： decision tree regression Metric validation Predicted fault Process metrics Product metrics Soft-computing Software fault prediction

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Modeling Investment decisions Through decision tree regression-A Behavioral Finance Theory Approach

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ELECTRONICS 2025年第8期14卷 1505-1505页

作者： Rad, Dana Cuc, Lavinia Denisia Croitoru, Gabriel Gomoi, Bogdan Cosmin Mazuru, Luminita Bilti, Raluca Simina Rusu, Sergiu Sinaci, Maria Barbu, Florentina Simona Aurel Vlaicu Univ Arad Fac Educ Sci Ctr Res Dev & Innovat Psychol Arad 310130 Romania Aurel Vlaicu Univ Arad Fac Econ Ctr Econ Res & Consultancy Arad 310130 Romania Valahia Univ Targoviste Fac Econ Targoviste 130004 Romania

This study examines the key factors influencing investment decisions through decision tree regression, grounded in behavioral finance theory. By analyzing a comprehensive dataset incorporating behavioral, demographic, and financial variables-including investment attitudes, decision-making behaviors, financial education, age, income, and education-this study identifies significant predictors of investment outcomes. While the model shows moderate predictive performance (R2 = 0.185;MAPE = 172.96%), it identifies hierarchical relationships among behavioral, cognitive, and demographic predictors. These results highlight the complexity of investment decisions and the need for integrative, behavioral-driven approaches in predictive modeling. Investment attitudes (25.88%), decision-making behaviors (19.53%), and financial education (16.68%) emerge as the most influential variables, while traditional demographic factors such as income and age have a lower impact. The hierarchical structure of the decision tree highlights critical decision-making patterns, particularly regarding speculative behaviors and investment attitudes. These findings challenge classical models of rationality by emphasizing the dominant role of behavioral factors in investment decision making. This study contributes to bridging computational modeling with financial economics, demonstrating the utility of decision tree regression in uncovering complex investor behavior. Practical implications include enhancing personalized financial advisory services and designing targeted financial literacy programs to improve decision-making efficiency. These insights, while exploratory, can guide future research and decision-support systems in behavioral finance.

关键词： decision tree regression investment decisions behavioral finance financial predictors computational modeling

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A Variable Selection Analysis for Soundscape Emotion Modeling Using decision tree regression and Modern Information Criteria

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IEEE ACCESS 2024年 12卷 92622-92634页

作者： Millan-Castillo, Roberto San Martino, Luca Morgado, Eduardo Univ Rey Juan Carlos URJC Dept Teoria Senal & Comunicac & Sistemas Telematic Fuenlabrada 28942 Spain

During the last decade, soundscape research has become one of the most active topics in Acoustics. This work provides a nonlinear variable selection analysis over the well-known dataset 'emo-soundscapes'. Namely, we provide a selection of the soundscape indicators using a nonlinear and nonparametric model as a regression tree method. Modern techniques (proposed in the literature) have been used, first for ranking the variables and then for choosing the effective number of features. We have also compared and discussed our results with those provided previously in the literature. This study, based on modern techniques in selecting the effective number of variables, confirms the result presented in previous recent work (but based on a linear model) that very parsimonious models should be considered (in the case of a nonlinear model, it is based on very few variables, from 2 to 4, depending on the output). All the results are obtained by analyzing a single dataset. As future research works, we plan to extend our study by also considering alternative datasets.

关键词： Soundscape emotion recognition decision tree regression ranking methods variable selection Soundscape emotion recognition decision tree regression ranking methods variable selection

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Estimating Pressure-Volume-Temperature Properties of Crude Oil Systems Using Boosted decision tree regression

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JOURNAL OF THE JAPAN PETROLEUM INSTITUTE 2022年第6期65卷 221-232页

作者： Almashan, Meshal Musaed Arsalan, Zolfaghari Narusue, Yoshiaki Morikawa, Hiroyuki Univ Tokyo Grad Sch Engn Bunkyo Ku 7-3-1 Hongo Tokyo 1138656 Japan Thermo Fisher Sci Mat & Struct Anal Houston TX 77478 USA

Machine learning has been successfully implemented in the estimation of reservoir fluid properties, competing with the empirical correlations used in this field. One of the most commonly used modeling schemes is the artificial neural network, which is known for its black-box problem. This study offers a different modeling approach that overcomes this limitation. The model provides accurate estimations and facilitates a deeper understanding of the key input parameters and their importance to the estimated results. It uses a boosted decision tree regression (BDTR) predictive modeling scheme to estimate the bubble point pressure (Pb) and oil formation volume factor at the bubble point pressure (Bob) as a function of oil and gas specific gravities, solution gas-oil ratio, and reservoir temperature. The built BDTR model exhibits higher accuracy and performance than previous machine learning models and the most commonly used empirical correlations for estimating Pb and Bob. The results indicate the higher efficacy of the developed model integrated with an imputation pre-processing step compared with the most commonly used empirical correlations in estimating Pb. This model brings significant predictive capability and versatility to datasets with multiple missing input features.

关键词： Predictive model Machine learning decision tree regression Pressure-volume-temperature Crude oil system Reservoir characterization

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A Robust Internet of Things-Based Aquarium Control System Using decision tree regression Algorithm

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IEEE ACCESS 2022年 10卷 56937-56951页

作者： Abdurohman, Maman Putrada, Aji Gautama Deris, Mustafa Mat Telkom Univ Sch Comp Bandung 40257 Indonesia Univ Tun Husein Onn Malaysia UTHM Dept Informat Batu Pahat 86400 Malaysia

The development of the Internet of Things (IoT) has shown significant contributions to many application areas, such as smart cities, smart homes, and smart farming, including aquarium control systems. Important things in an aquarium system are the level of ammonia in the water and the temperature of the water. Other research proposes several systems to make the aquarium control system robust for the aquarium monitoring and control system. However, those systems have weaknesses;namely, the user must actively access information to the server. This paper proposes a robust aquarium control system using the decision tree regression (DTR) algorithm. The development of this system was to overcome the problem of aquarium control by remote users. An accurate and real-time system is needed to monitor the aquarium so that it does not reach dangerous and critical points, such as in the case of an increase in water temperature. We did tests by developing an aquarium system connected to a server and an application that acts as a controller. Our measurements check the delay of sending data from the sensor to the server, process delay, actuator delay, user delay, and delay in reaching the aquarium's critical point. The measurement of the system's robustness is by calculating the probability of the information arrival to the user in the period of the critical point compared to the time needed to reach the critical point. Furthermore, we also made an analytical model based on the probability density function of the delay covered in this system. Analytically and experimentally, we show that the system can meet the needs of aquarium monitoring and control in an IoT-based environment.

关键词： Temperature sensors Servers Fish Delays Monitoring Temperature measurement Forecasting Internet of Things analytical model probability density function robustness aquaculture decision tree regression

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decision tree regression model to predict low-rank coal moisture content during convective drying process

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INTERNATIONAL JOURNAL OF COAL PREPARATION AND UTILIZATION 2020年第8期40卷 505-512页

作者： Pekel, Engin Akkoyunlu, Mehmet Cabir Akkoyunlu, Mustafa Tahir Pusat, Saban Hitit Univ Dept Ind Engn Corum Turkey Karamanoglu Mehmetbey Univ Dept Mech & Met Technol Karaman Turkey Necmettin Erbakan Univ Dept Energy Syst Engn Meram Konya Turkey Yildiz Tech Univ Dept Mech Engn Istanbul Turkey

Coal is still a significant energy source for the world. Due to the utilization of low-rank coal, drying is a key issue. There are lots of attempts to develop efficient drying processes. The most prominent method seems as thermal drying. For thermal drying processes, the most important subject is the coal moisture content change with time. In this study, convective drying experiments were utilized to develop a new model based on decision tree regression method to predict coal moisture content. The developed model gives satisfactory results in prediction of instant coal moisture content with changing drying conditions. With the decision tree depth of six, the best test results were achieved as 0.056 and 0.802 for MSE and R-2 analyses, respectively.

关键词： decision tree regression coal drying moisture content low-rank coal

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Estimation of soil moisture using decision tree regression

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THEORETICAL AND APPLIED CLIMATOLOGY 2020年第3-4期139卷 1111-1119页

作者： Pekel, Engin Hitit Univ Dept Ind Engn Fac Engn Corum Turkey

Soil moisture (SM) is a significant factor in the climate system. The accurate determination of SM has high importance in food production to satisfy the increasing demand for food and the chemical processes of soil. This paper applies decision tree regression to estimate SM considering different parameters including air temperature, time, relative humidity, and soil temperature. The presented method holds a mighty advantage to determine SM since the stimulant of the decision tree regression is an algorithm that generates a decision tree from given instances. Besides, usage of decision tree regression provides an opportunity to save time. Numerical results show that the presented method offers a high coefficient of determination value (R-2), low mean squared error (MSE), and mean absolute error (MAE). The depth of the decision tree equals to five by providing higher fitness values than other depth levels. The best fitness values in the training stage are 0.00019, 0.007, and 0.842 for MSE, MAE, and R-2, respectively. In conclusion of the paper, applied decision tree regression can handle the data of SM estimation in satisfying fitness criterion.

关键词： decision tree regression Estimation Learning Soil moisture

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Development of heavy-duty vehicle representative driving cycles via decision tree regression

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TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2021年 95卷 102843-102843页

作者： Zhang, Chen Kotz, Andrew Kelly, Kenneth Rippelmeyer, Luke Natl Renewable Energy Lab Golden CO 80401 USA Toyota Motor North Amer Plano TX USA

Previously, researchers who developed representative driving cycles mainly focused on light-duty vehicles and only considered vehicle speed and related derivations. In this paper, we propose a novel approach to develop representative cycles for heavy-duty vehicles. By implementing decision tree regression (DTR) to the Fleet DNA on-road vehicle data, a broader set of metrics, such as engine power and fuel consumption, can be used for more robust cycle development. Additionally, the influence of each metric on the regression target is also accounted for by a weighted number derived through the DTR to enhance the representativenss of the developed cycle. As case studies, we applied the proposed method to five heavy-duty vocations (drayage, long haul, regional haul, local delivery, and transit bus) and derived the most representative cycle, as well as four extreme cycles (maximal energy consumption, maximal power-weighted work, maximal fraction of high speed, and minimal fuel economy) to advance the related alternative powertrain design.

关键词： Heavy-duty vehicle Powertrain electrification decision tree regression Representative driving cycle On-road data

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An Intelligent Boosting and decision-tree-regression-Based Score Prediction (BDTR-SP) Method in the Reform of Tertiary Education Teaching

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INFORMATION 2023年第6期14卷 317-317页

作者： Zhu, Ling Liu, Guangyu Lv, Shuang Chen, Dongjie Chen, Zhihong Li, Xiang Zhejiang Univ Finance & Econ Sch Artificial Intelligence & Informat Management Hangzhou 310012 Peoples R China Hangzhou Dianzi Univ Sch Automat Key Lab IoT & Informat Fus Technol Hangzhou 310005 Peoples R China

The reform of tertiary education teaching promotes teachers to adjust timely teaching plans based on students' learning feedback in order to improve teaching performance. Thefore, learning score prediction is a key issue in process of the reform of tertiary education teaching. With the development of information and management technologies, a lot of teaching data are generated as the scale of online and offline education expands. However, a teacher or educator does not have a comprehensive dataset in practice, which challenges his/her ability to predict the students' learning performance from the individual's viewpoint. How to overcome the drawbacks of small samples is an open issue. To this end, it is desirable that an effective artificial intelligent tool is designed to help teachers or educators predict students' scores well. We propose a boosting and decision-tree-regression-based score prediction (BDTR-SP) model, which relies on an ensemble learning structure with base learners of decision tree regression (DTR) to improve the prediction accuracy. Experiments on small samples are conducted to examine the important features that affect students' scores. The results show that the proposed model has advantages over its peer in terms of prediction correctness. Moreover, the predicted results are consistent with the actual facts implied in the original dataset. The proposed BDTR-SP method aids teachers and students to predict students' performance in the on-going courses in order to adjust the teaching and learning strategies, plans and practices in advance, enhancing the teaching and learning quality. Therefore, the integration of information technology and artificial intelligence into teaching and learning practices is able to push forward the reform of tertiary education teaching.

关键词： teaching reform small samples score prediction ensemble learning decision tree regression

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A Novel decision tree regression-Based Fault Distance Estimation Scheme for Transmission Lines

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IEEE TRANSACTIONS ON POWER DELIVERY 2017年第1期32卷 234-245页

作者： Swetapadma, Aleena Yadav, Anamika NIT Dept Elect Engn Raipur 492010 Chattisgarh India

In this paper, a decision tree regression (DTR)-based fault distance estimation scheme for double-circuit transmission lines is presented. Fault location is estimated using the information obtained from fault events data. The DTR was chosen because it requires less training time, offers greater accuracy with a large data set, and robustness than all other techniques like artificial neural networks, support vector machines, adaptive neurofuzzy inference systems, etc. Hitherto, DT has been used for fault detection/classification, but it has not been used for fault location. Three-phase current and voltage signals measured at one end of the line are used as inputs to a fault-location network. The proposed method does not require a communication link as it uses only one-end measurements. Signals are processed with two signal-processing techniques-discrete Fourier transforms and discrete wavelet transform. A comparative study of both techniques has been carried out to observe the effect of signal processing on the fault-location estimation method. The proposed method is tested on three test systems, namely: 1) the 2-bus;2) the WSCC-9-bus;and 3) the IEEE 14-bus test systems. The test results confirm that the proposed DTR-based algorithm is not affected by the variation in fault type, fault location, fault inception angle, fault resistance, prefault load angle, SCC, load variation, and line parameters. The proposed scheme is relatively simple and easy in comparison with complex equation-based fault-location estimation methods.

关键词： decision tree regression discrete Fourier transform fault distance estimation wavelet transforms

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