Decision Tree-based Method Research Articles

A Decision Tree-Based Method for Evaluating the Remanufacturability of Used Parts

A Decision Tree-Based Method for Evaluating the Remanufacturability of Used Parts

A Novel Skin Disease Detection Technique Using Machine Learning

Skin sicknesses present critical medical care difficulties around the world, requiring precise and opportune location for successful therapy. AI became promising stuff for computerizing the discovery and characterization of skin illnesses. This study presents a clever methodology that uses the choice tree strategy for skin sickness location. In computerized location, we utilize an exhaustive dataset containing different skin sickness pictures, including melanoma, psoriasis, dermatitis, and contagious diseases. Dermatologists skillfully mark the dataset, guaranteeing solid ground truth for precise grouping. Preprocessing strategies like resizing, standardization, and quality improvement are applied to set up the symbolism for the choice tree calculation. Then, we remove applicable elements from the preprocessed pictures, enveloping surface, variety, and shape descriptors to catch infection explicit examples successfully. The choice tree model is prepared utilizing these removed elements and the named dataset. Utilizing the choice tree's capacity to learn progressive designs and choice principles, our methodology accomplishes an elevated degree of exactness in grouping skin sicknesses. Extensive experiments and evaluations on a dedicated validation set demonstrate the effectiveness of our decision tree-based method, achieving a classification accuracy of 96%. Our proposed method provides a reliable and automated solution for skin disease detection, with potential applications in clinical settings. By enabling early and accurate diagnoses, our approach has the capacity to improve patient outcomes, trim down healthcare overheads, and alleviate the burden on dermatologists.

Molecular diagnosis of 5 silkworm strains endemic to South Korea using single-nucleotide polymorphisms selected from whole-genome sequences.

The domesticated silkworm, Bombyx mori Linnaeus (Lepidoptera: Bombycidae), often poses a challenge in strain identification due to similarities in morphology and genetic background. In South Korea, around 40 silkworm strains are classified as premium, including 5 endemic tri-molting strains: Goryeosammyeon, Sammyeonhonghoeback, Hansammyeon, Sun7ho, and Sandongsammyeon. These strains have potential for breeding programs in response to emerging industry demands, necessitating a reliable strain identification method. In this study, we established a molecular diagnosis approach for these 5 strains. We selected 2-4 single-nucleotide polymorphisms (SNPs) for each strain from whole-genome sequences of 39 strains, encompassing 37 previously studied and 2 newly added. These SNPs were utilized to construct decision trees for each endemic strain identification. The SNPs can be used to distinguish each target strain from the 38 nontarget strains by the tetra-primer amplification refractory mutation system-polymerase chain reaction, with the exception of HMS which needs the addition of PCR-restriction fragment length polymorphism method at the final step. This decision tree-based method using genomic SNPs, coupled with the 2 typing methods, produced consistent and accurate results, providing 100% accuracy. Additionally, the significant number of remaining SNPs identified in this study could be valuable for future diagnosis of the other strains.

Simultaneous Estimation of Azimuth and Elevation Angles Using a Decision Tree-Based Method.

This study addresses the problem of accurately predicting azimuth and elevation angles of signals impinging on an antenna array employing Machine Learning (ML). Using the information obtained at a receiving system when a transmitter's signal hits it, a Decision Tree (DT) model is trained to estimate azimuth and elevation angles simultaneously. Simulation results demonstrate the robustness of the proposed DT-based method, showcasing its ability to predict the Direction of Arrival (DOA) in diverse conditions beyond the ones present in the training dataset, i.e., the results display the model's generalization capability. Additionally, the comparative analysis reveals that DT-based DOA estimation outperforms the state-of-the-art MUltiple SIgnal Classification (MUSIC) algorithm. Our results demonstrate an average reduction of over 90% in the prediction error and 50% in the prediction time achieved by our proposal when compared to the MUSIC algorithm. These results establish DTs as competitive alternatives for DOA estimation in signal reception systems.

Development of Machine Learning-Based Web System for Estimating Pleural Effusion Using Multi-Frequency Bioelectrical Impedance Analyses.

Transthoracic impedance values have not been widely used to measure extravascular pulmonary water content due to accuracy and complexity concerns. Our aim was to develop a foundational model for a novel system aiming to non-invasively estimate the intrathoracic condition of heart failure patients. We employed multi-frequency bioelectrical impedance analysis to simultaneously measure multiple frequencies, collecting electrical, physical, and hematological data from 63 hospitalized heart failure patients and 82 healthy volunteers. Measurements were taken upon admission and after treatment, and longitudinal analysis was conducted. Using a light gradient boosting machine, and a decision tree-based machine learning method, we developed an intrathoracic estimation model based on electrical measurements and clinical findings. Out of the 286 features collected, the model utilized 16 features. Notably, the developed model demonstrated high accuracy in discriminating patients with pleural effusion, achieving an area under the receiver characteristic curves (AUC) of 0.905 (95% CI: 0.870-0.940, p < 0.0001) in the cross-validation test. The accuracy significantly outperformed the conventional frequency-based method with an AUC of 0.740 (95% CI: 0.688-0.792, and p < 0.0001). Our findings indicate the potential of machine learning and transthoracic impedance measurements for estimating pleural effusion. By incorporating noninvasive and easily obtainable clinical and laboratory findings, this approach offers an effective means of assessing intrathoracic conditions.

Just-in-time defect prediction for mobile applications: using shallow or deep learning?

Just-in-time defect prediction (JITDP) research is increasingly focused on program changes instead of complete program modules within the context of continuous integration and continuous testing paradigm. Traditional machine learning-based defect prediction models have been built since the early 2000s, and recently, deep learning-based models have been designed and implemented. While deep learning (DL) algorithms can provide state-of-the-art performance in many application domains, they should be carefully selected and designed for a software engineering problem. In this research, we evaluate the performance of traditional machine learning algorithms and data sampling techniques for JITDP problems and compare the model performance with the performance of a DL-based prediction model. Experimental results demonstrated that DL algorithms leveraging sampling methods perform significantly worse than the decision tree-based ensemble method. The XGBoost-based model appears to be 116 times faster than the multilayer perceptron-based (MLP) prediction model. This study indicates that DL-based models are not always the optimal solution for software defect prediction, and thus, shallow, traditional machine learning can be preferred because of better performance in terms of accuracy and time parameters.

Fast CU Division Pattern Decision Based on the Combination of Spatio-Temporal Information

In order to satisfy the growing need for high-quality video, VVC comes with more efficient coding performance. According to statistical analysis, the level of coding complexity in VVC is tenfold greater compared to that of HEVC, so it is our main goal to study that what methods can be employed to decrease the time complexity of VVC. CU split in intra-frame modes requires the split mode decision by RD loss calculation, and the process of coding makes it to calculate RD calculation for all possible mode combinations, which is an important area that brings complexity to video coding, so in order to achieve our goal. Initially, we introduce an optimal depth prediction algorithm for Coding Units (CUs) by leveraging temporal combination. This algorithm collects depth information of CUs to predict the coding depth of CU blocks. Additionally, we suggest a decision tree-based method for CU split mode decision. With this method, we can make a decision on the CU split mode within the obtained split depth, reducing the time complexity of coding. This decision is based on the predictions from the first algorithm. The results demonstrate that our algorithm achieves superior performance over state-of-the-art methods in terms of computational complexity and compression quality. Compared to the VVC reference software (VTM), our method saves an average of 53.92% in coding time and improves the BDBR by 1.74%. These findings suggest that our method is highly effective in improving both computational efficiency and compression quality.

PREDIKSI PASIEN DENGAN PENYAKIT KARDIOVASKULAR MENGGUNAKAN RANDOM FOREST

Cardiovascular disease is one of the deadliest diseases in the world. This is evidenced by data released by WHO which shows around 18 million deaths. This disease causes the cessation of the heartbeat which is the main source of life for the human body.This disease is caused by various things including an unhealthy lifestyle. Examples are consuming cigarettes and alcohol. In addition, it is also caused by other factors, namely health problems such as high blood pressure, cholesterol, diabetes, depression, or anxiety. The cardiovascular disease tends to be difficult to cure, therefore a precise and accurate prediction is needed in diagnosing patients. One method of making predictions is using machine learning techniques. In machine learning, there are various methods that can be used, one of which is the decision tree-based method, namely random forest. Before the random forest is implemented to create a model, the data is pre-processed by normalizing and applying cross-validation with k-fold = 10. The prediction results with the random forest in this study provide an accuracy of 98%. This accuracy is higher when compared to previous studies with the same dataset, namely 96.75% using the ensemble method and 91.61% with logistic regression. On this basis, it proves that the random forest can be used to predict cardiovascular disease.&#x0D; Key Words: cardiovascular disease, tree model, random forest, machine learning.

Application of random forest-based decision tree approach for modeling fully developed turbulent flow in rough pipes

A random forest (RF) -based decision tree programming methodology was aimed for modeling fully developed turbulent flow conditions in rough pipes. In the present computational study, a flexible RF-based soft-computing strategy was applied for the estimation of the required pipe diameter (D) and Darcy–Weisbach friction factor (λ or f) obtained from the iterative solution of the implicit Colebrook–White equation for five basic pipeline design variables considered in sizing problems (Type 3) of pipe distribution systems. The prediction performance of the implemented RF-based model was assessed more than 15 different statistical goodness-of-fit parameters and useful mathematical diagrams such as box-and-whisker-plots and spread plots. The statistical metrics corroborated the superiority of the RF-based approach in predicting both the required pipe diameter (R2 = 0.9793, MAE = 0.0287 m, RMSE = 0.03833 m, SEE = 0.0326 m, IA or WI = 0.9933, CV(RMSE) or SI = 0.0595, NSE = 0.9753, LMI = 0.8482, and AIC = -1954.6438 for the testing dataset) and friction factor (R2 = 0.9576, MAE = 0.0011, RMSE = 0.0023, SEE = 0.0018, IA or WI = 0.9851, CV(RMSE) or SI = 0.0660, NSE = 0.9478, LMI = 0.8500, and AIC = -3646.7124 for the testing dataset). The descriptive statics suggested that the 25% percentile values (Q1), median values (Q2), and 75% percentile values (Q3) of RF-predicted values of D and λ and the corresponding actual values of these responses were found to be very close. The proposed RF-based model was also tested against additional some dataset obtained from the relevant literature. The validation results indicated that the applied decision tree-based method produced realistic estimations and acceptable statistics (i.e., R2 = 0.9624, MAE = 0.0598 m, and RMSE = 0.0708 m for D values, and R2 = 0.9130, MAE = 0.0043, RMSE = 0.0052 for λ values) even at extreme L values greater than 2000 m. This study demonstrated the importance and ability of the applied soft-computing strategy to accurately predict D and λ values and eliminated error-prone steps of the traditional iterative approach.

Pseudo-spectral optimization and model predictive control of vehicle shift process with dual clutch transmission

To improve the shift quality of dual clutch transmission (DCT) vehicles and reduce the jerk and sliding friction in the shift process, a real-time planning of clutch optimal engagement trajectory and control method in the shift process is proposed. This method combines pseudo-spectral optimization, machine learning, and model predictive control. First, considering the jerk, sliding friction work, and shift time as the optimization indices, the adaptive pseudo-spectral method is employed to optimize the engagement trajectory of the clutch during the shift process. Based on the optimal trajectory data set, a gradient boosting decision tree-based real-time planning method for the clutch target engagement trajectory is proposed. Second, a model predictive control strategy for the shift process is proposed based on the DCT system model to track the optimal target trajectory in real-time. Simulations and experiments reveal that the proposed target engagement trajectory planning method can plan the clutch target engagement trajectory for various accelerator pedal openings and initial clutch speed states in real-time, and the proposed model predictive control method can accurately track the target trajectory. Compared with the original vehicle strategy, under 35% and 65% accelerator pedal opening, the maximum absolute value of jerk produced by the proposed strategy was reduced by 31.06% and 31.46%, respectively, and the sliding friction work was reduced by 22.87% and 23.24%, respectively. The shift times of the proposed strategy under 35% and 65% accelerator pedal openings were 19.12% and 20.69% lower than that of the original vehicle strategy, respectively.

A decision tree-based method for detecting middle school students' behaviour characteristics in online English learning

In order to solve the problem of low accuracy and long detection time caused by poor feature extraction effect of online English learning students' behavioural characteristics detection, this paper proposes a method of online English learning students' behaviour characteristics detection based on decision tree. Firstly, the concept and structure of decision tree are analysed, and the classification steps are designed. Secondly, weighted principal component analysis was used to extract the behaviour characteristics of students. Then, the characteristic data is standardised. Finally, the C4.5 decision tree algorithm is used to construct a student behaviour feature detection model to detect students' behaviour characteristics in online English learning. The experimental results show that the feature detection rate of the proposed method is as high as 99.5%, the accuracy is 96.2%, and the detection time is 19.8 s. Therefore, the feature detection effect of the proposed method is good, the accuracy is high, and the detection time is effectively shortened.

A Decision Tree-Based Online Traffic Classification Method for QoS Routing in Data Center Networks

In data center networks, how to ensure the quality of service (QoS) of traffic effectively has become an important optimization goal. Traffic classification has been widely studied to guarantee network flow QoS since it provides a basis for the network performance level requirement of different flow types. However, traditional network traffic classification methods, which can only identify traffic type offline, have no ability for online classification and the in-time QoS guarantee. To solve this problem, we propose an online traffic classification for QoS routing (OCQR) framework based on programmable data plane in this paper. Firstly, in order to improve the accuracy of flow classification and reduce the impact on line-rate packet forwarding, the way of machine learning model offline training and online deployment is used to classify the network flow in real time. Based on the hardware foundation of programmable switch and the complexity of machine learning algorithm, a CART decision tree model is used to classify network flows. Secondly, in order to actually deploy and run the decision tree model in the programmable switch with limited computing and storage resources, we prune the decision tree model and optimize the leaf nodes to reduce the size of the decision tree and improve the classification recall rate. Finally, by building a P4 simulation environment, the recall rate of OCQR’s classified scheduling of some application flows reaches more than 98%, which is consistent with the evaluation results of the offline dataset. At the same time, compared with the classification method based on SDN, OCQR does not require additional bandwidth occupation, so it has less impact on network performance.

Assessing swidden land use in Myanmar by decision tree-based detection method using landsat imagery

Swidden agriculture is a common land use found in the mountainous regions, especially in Southeast Asia. In Myanmar, the swidden agriculture has been practicing as an important livelihood strategy of millions of people, mainly by the ethnic groups. However, the extent of swidden agriculture in Myanmar is still in question. Therefore, we attempted to detect swidden patches and estimate the swidden extent in Myanmar using free available Landsat images on Google Earth Engine in combination with a decision tree-based plot detection method. We applied the commonly used indices such as dNBR, RdNBR, and dNDVI, statistically tested their threshold values to select the most appropriate combination of the indices and thresholds for the detection of swidden, and assessed the accuracy of each set of index and thresholds using ground truth data and visual interpretation of sample points outside the test site. The results showed that dNBR together with RdNBR, slope and elevation demonstrated higher accuracy (84.25%) compared to an all-index combination (dNBR, RdNBR, dNDVI, slope, and elevation). Using the best-fit pair, we estimated the extent of swidden at national level. The resulting map showed that the total extent of swidden in Myanmar was about 0.1 million ha in 2016, which is much smaller than other previously reported figures. Also, swidden patches were mostly observed in Shan State, followed by Chin State. In this way, this study primarily estimated the total extent of swidden area in Myanmar at national level and proved that the use of a decision tree-based detection method with appropriate vegetation indices and thresholds is highly applicable to the estimation of swidden extent on a regional basis. Also, as Myanmar is the largest country in mainland Southeast Asia in area with a great majority of the population living in rural areas, and many in the mountains, its land resources are of great relevance to the people’s livelihoods and thereby the nation’s progress. Therefore, this study will contribute to sustainable land management planning on both regional and national scale.

Poyang Lake Wetland Classification Using Time-Series ENVISAT ASAR Data and Beijing-1 Imagery

Beijing-1 and ENVISAT ASAR images were used to classify wetland aquatic macrophytes in terms of their plant functional types (PFTs) over the Poyang Lake region, China. Speckle noise filtering, systematic sensor calibration within the same polarization or between different polarizations, and accurate geo-registration were applied to the time-series SAR data. As a result, time-series backscattering data, which is described as permittivity curves in this paper, were obtained. In addition, time-series indices, described as phenological curves, were derived from Beijing-1 time-series images in the classification experiment. Based on these two curves, a rule-based classification strategy was developed to extract wetland information from the combined SAR and optical data. In the rule-based wetland classification method, DEM data, submersion time index, temporal Beijing-1 images, time-series normalized difference vegetation index (TSNDVI) images, principal component analysis (PCA), and temporal ratio of ASAR time-series images were used. In addition, a decision tree-based method was used to map the wetlands. Conclusions include the following: (1) after the preprocessing of ASAR data, it was possible to satisfactorily separate different aquatic plant functional types; (2) hydrophytes from different PFTs exhibited distinct phenological, structural, moisture, and roughness characteristics due to the impact of the annual inundation of Poyang Lake wetland; and (3) more accurate results were obtained with the rule-based method than the decision tree (DT) method. Producer’s and user’s accuracy calculated from test samples in the classification results indicate that the DT method can potentially be used for mapping aquatic PFTs, with overall producer’s accuracy exceeding 80% and higher user’s accuracy for aquatic bed wetland PFTs. A comparison of producer’s and user’s accuracy from the rule-based classification increased from 3 to 12% and 7 to 26%, respectively, for different aquatic PFTs.

Development of a Prediction Method of Cell Density in Autotrophic/Heterotrophic Microorganism Mixtures by Machine Learning Using Absorbance Spectrum Data.

Microflora is actively used to produce value-added materials in industry, and each cell density should be controlled for stable microflora use. In this study, a simple system evaluating the cell density was constructed with artificial intelligence (AI) using the absorbance spectra data of microflora. To set up the system, the prediction system for cell density based on machine learning was constructed using the spectra data as the feature from the mixture of Saccharomyces cerevisiae and Chlamydomonas reinhardtii. As the results of predicting cell density by extremely randomized trees, when the cell densities of S. cerevisiae and C. reinhardtii were shifted and fixed, the coefficient of determination (R2) was 0.8495; on the other hand, when the cell densities of S. cerevisiae and C. reinhardtii were fixed and shifted, the R2 was 0.9232. To explain the prediction system, the randomized trees regressor of the decision tree-based ensemble learning method as the machine learning algorithm and Shapley additive explanations (SHAPs) as the explainable AI (XAI) to interpret the features contributing to the prediction results were used. As a result of the SHAP analyses, not only the optical density, but also the absorbance of the Soret and Q bands derived from the chloroplasts of C. reinhardtii could contribute to the prediction as the features. The simple cell density evaluating system could have an industrial impact.

A decision tree-based method for ordinal classification problems

In ordinal classification problems, the class value exhibits a natural order. Usually, these problems are solved as multiclass classification problems while discarding the ordering form of the class. Recently, several research studies have proposed novel methods for ordinal classification problems while aiming to predict a predefined objective value. These methods are based on modification of the splitting criteria of decision tree-based algorithms. These methods consider the ordinal nature of the data and the magnitude of the potential classification error from a predefined predicted objective. This research aims to consolidate these methods and generalize them for any decision tree-based method while constructing decision trees according to any predefined objective value calculated from the training dataset. Furthermore, to evaluate the proposed method, a comprehensive experimental study is performed based on known ordinal datasets. The proposed method is found to significantly outperform its counterpart nonordinal models. This observation confirms that ordering information benefits ordinal methods and improves their performance. Additionally, the ordinal decision tree-based methods achieved competitive performance compared to state-of-the-art ordinal techniques. The results are validated with various performance measures that are commonly used for ordinal and non-ordinal classification problems.

Development and evaluation of hybrid deep learning long short-term memory network model for pan evaporation estimation trained with satellite and ground-based data

Evaporation, as a core process within the global hydrological cycle, requires reliable methods to monitor its variation, for decision-making in agriculture, irrigation systems and dam operations, also in other areas of hydrology and water resource management. Accurate monitoring of pan evaporation (Ep) is one the most popular approaches to understand the evaporative process. This work aims to construct a hybrid Long Short-Term Memory (LSTM) predictive model that is coupled with Neighbourhood Component Analysis for feature selection to predict Ep in drought-prone regions in Queensland, Australia (Amberley, Gatton, Oakey, & Townsville). Utilizing the daily-scale dataset [31 August 2002 to 22 September 2020], the performance of the proposed deep learning (DL) hybrid model, denoted as NCA-LSTM, is compared with competitive benchmark models, i.e., standalone LSTM, other types of DL, single hidden layer neuronal architecture and decision tree-based method. The testing results reveal the lowest Relative Root Mean Square Error ≤20%, Absolute Percentage Bias ≤14.5% and the highest Kling-Gupta Efficiency ≥87% attained by the NCA-LSTM hybrid model (relative to benchmark models) tested for Amberley, Gatton, and Oakey sites. In respect to the predictive efficiency, the proposed NCA-LSTM hybrid model, improved with feature selection, outperforms all benchmark models, indicating its future utility in the prediction of daily Ep. In practical sense, the predictive model developed for Ep estimation provides an accurate estimation of evaporative water loss in hydrological cycle and therefore, can be implemented in areas of irrigation management, planning of irrigation-based agriculture, and mitigation of financial losses to agricultural and related sectors where, regular monitoring and forecasting of water resources are a vital part of sustainable livelihood and business.

Adding Interpretability to Neural Knowledge DNA

This paper proposes a novel approach that adds the interpretability to Neural Knowledge DNA (NK-DNA) via generating a decision tree. The NK-DNA is a promising knowledge representation approach for acquiring, storing, sharing, and reusing knowledge among machines and computing systems. We introduce the decision tree-based generative method for knowledge extraction and representation to make the NK-DNA more explainable. We examine our approach through an initial case study. The experiment results show that the proposed method can transform the implicit knowledge stored in the NK-DNA into explicitly represented decision trees bringing fair interpretability to neural network-based intelligent systems.

Decision Tree-Based Data Stratification Method for the Minimization of the Masking Effect in Adverse Drug Reaction Signal Detection

Data masking is an inborn defect of measures of disproportionality in adverse drug reactions signal detection. Some improved methods which used gender and age for data stratification only considered the patient-related confounding factors, ignoring the drug-related influencing factors. Due to a large number of reports and the high proportion of antibiotics in the Chinese spontaneous reporting database, this paper proposes a decision tree-stratification method for the minimization of the masking effect by integrating the relevant factors of patients and drugs. The adverse drug reaction monitoring reports of Jiangsu Province in China from 2011 to 2018 were selected for this study. First, the age division interval was determined based on the statistical analysis of antibiotic-related data. Secondly, correlation analysis was conducted based on the patient’s gender and age respectively with the drug category attributes. Thirdly, the decision tree based on age and gender was constructed by the J48 algorithm, which was used to determine if drugs belonged to antibiotics as a classification label. Fourthly, some performance evaluation indicators were constructed based on the data of drug package inserts as a standard signal library: recall, precision, and F (the arithmetic harmonic mean of recall and precision). Finally, four experiments were carried out by means of the proportional reporting ratio method: non-stratification (total data), gender-stratification, age-stratification and decision tree-stratification, and the performance of the signal detection results was compared. The experimental results showed that the decision tree-stratification was superior to the other three methods. Therefore, the data-masking effect can be further minimized by comprehensively considering the patient and drug-related confounding factors.

A hybrid machine learning method for increasing the performance of network intrusion detection systems

The internet has grown enormously for many years. It is not just connecting computer networks but also a group of devices worldwide involving big data. The internet provides an opportunity to make various innovations for any sector, such as education, health, public facility, financial technology, and digital commerce. Despite its advantages, the internet may contain dangerous activities and cyber-attacks that may happen to anyone connected through the internet. To detect any cyber-attack intrudes on the network system, an intrusion detection system (IDS) is applied, which can identify those incoming attacks. The intrusion detection system works in two mechanisms: signature-based detection and anomaly-based detection. In anomaly-based detection, the quality of the machine learning model obtained is influenced by the data training process. The biggest challenge of machine learning methods is how to build an appropriate model to represent the dataset. This research proposes a hybrid machine learning method by combining the feature selection method, representing the supervised learning and data reduction method as the unsupervised learning to build an appropriate model. It works by selecting relevant and significant features using feature importance decision tree-based method with recursive feature elimination and detecting anomaly/outlier data using the Local Outlier Factor (LOF) method. The experimental results show that the proposed method achieves the highest accuracy in detecting R2L (i.e., 99.89%) and keeps higher for other attack types than most other research in the NSL-KDD dataset. Therefore, it has a more stable performance than the others. More challenges are experienced in the UNSW-NB15 dataset with binary classes.