Decision Tree Classification Technique Research Articles

Utilizing Extremely Fast Decision Tree (EFDT) Algorithm to Categorize Conflict Flow on a Software-Defined Network (SDN) Controller

Software-Defined Networks (SDNs) provide a contemporary approach to networking technology, offering a versatile and dynamically efficient network architecture for enhanced surveillance and performance. However, SDN architectures may encounter flow conflicts. These conflicts arise when modifications are made to specific flow properties, such as priority, match field, and action. Despite the existence of recommended solutions, the process of resolving conflicts in SDN continues to encounter difficulties. This study proposes an Extremely Fast Decision Tree (EFDT) classification technique to detect and categorize conflicts inside the flow table. The novelty of this method is based on the development of an accurate and effective machine-learning technique implemented on the Ryu controller plane and validated using the Mininet simulator. The effectiveness and efficiency of the proposed method were evaluated using various indicators, demonstrating superior performance in recognizing and categorizing conflict flow types in all flow sizes ranging from 10,000 to 100,000.

A novel diagnosis method for schizophrenia based on globus pallidus data

This research aims to diagnose schizophrenia with machine learning-based algorithms. Bayesian neural network, logistic regression, decision tree, k-nearest neighbor, and gaussian kernel classification techniques are investigated to diagnose schizophrenia with data from 125 persons. This study showed that left lateral ventricles and left globus pallidus volumes and their percentages in the brain were significantly lower than HCs in FEP patients. Using brain volumes, we were able to diagnose FEP with an accuracy of 73.6 % via logistic regression and with an accuracy of 86.4 % using the SVM kernel classifier method. Therefore, brain volumes can be used to diagnose FEP with the SVM kernel classifier method.

A decision tree-based damage estimation approach for preliminary seismic assessment of reinforced concrete buildings

This study aims to introduce an earthquake-induced damage classification approach for seismic vulnerability assessment of reinforced concrete buildings. Through the use of the damage data collected from post-earthquake inspections after the 2003 Bingöl Earthquake in Turkey, two models were constructed by the decision tree classification technique considering nine building-specific features as the estimation variables in the analysis. The first model was developed for the prediction of the observed damage states of the buildings, whereas the second one concerning the life safety level assessment, was proposed for distinguishing the extremely vulnerable buildings for seismic prioritization. In the validation process, the leave-one-out cross validation technique was adopted to deal with the small sample size of the building inventory. Among the estimation variables, the priority index and the existence of short columns were found to have the highest importance in classification. Results have revealed that the proposed model for life safety level assessment was capable of discriminating the cluster of severely damaged and collapsed buildings from the entire database with an accuracy of 70.59%. Hence, the damage classification approach adopted in this study has the potential for improving effective tools for seismic risk assessment of the existing buildings.

Disease Detection System in Tobacco Leaves based on Edge Detection with Decision Tree Classification Method

Indonesia has long been known as an agricultural country, one of Indonesia's best agricultural products is tobacco. Tobacco with good quality can be seen from the leaves, in fact the diseases that attack tobacco are of various types which can be seen from the changes in the tobacco leaves starting from the seeding and planting period. In the tobacco growing period, it is divided into two major parts, namely seeding period and the planting period, so that diseases that attack tobacco are also divided into two, namely diseases that attack during seeding and planting. This research is limited to diseases that attack tobacco at the time of planting, because at the time of seedling the tobacco has not yet produced leaves. When tobacco enters the planting period, at this time tobacco leaves begin to form. Good care is needed at this time such as fertilization, nutrition, vitamins, and pest control in order to obtain healthy tobacco so that tobacco is not susceptible to disease. Tobacco that lacks nutritional intake will be susceptible to diseases including fungi, bacteria, and viruses. The disease attack on tobacco has its own characteristics that appear on tobacco leaves. Early detection of the disease is very important so that disease control can be precise and the spread of the disease can be prevented so as not to cause endemic. In this research, an early detection system of tobacco leaf disease based on image processing will be designed. Normalization image, grayscale technique, folllowed by edge detection will be applied in these image so that from here the entropy, energy, and inertia values of the image can be obtained using statistical measures, and the last one using the decision tree classification technique can be classified as uninfected leaf or infected leaf. In this study, feature extraction from images of tobacco leaves that are not infected with the virus using grayscale techniques followed by edge detection produces an average statistical measure with entropy (h) values between 2,341 to 2,676, energy (e) values between 6,112 to 6,665, and inertia values. (i) between 3,322 to 3,576, while for leaves infected with the virus the average value of entropy (h) is between 4,543 to 5,576, the average value of energy (e) is between 12,212 to 13,455, and the average value of inertia (i) between 5,343 to 6,597.

(Retracted) Hurricane damage assessment in satellite images using hybrid VGG16 model

Hurricanes are one of the most disastrous natural phenomena occurring on Earth that cause loss of human lives and immense damage to property. A damage assessment method has been proposed for damage caused to buildings due to Hurricane Harvey that hit the Texas region in the year 2017. The aim of our study is to predict if there is any damage to the buildings present in the postdisaster satellite images. Principal component analysis has been used for the visualization of data. The VGG16 model has been used for extracting features from the input images. K-nearest neighbor (KNN), logistic regression, decision tree, random forest, and XGBoost classification techniques have been used for classification of the images whose features have been extracted from VGG16. Best accuracy of 97% is obtained by KNN classifier for the balanced test set, and accuracy of 96% is obtained by logistic regression for the unbalanced test set.

The Implementation of Decision Tree Classification Techniques to Predict the Duration of Students Completing the Thesis at PTIK FKIP UNS

<span>This paper aims to determine the reasons why students take a long time in compiling their thesis. The slowness of students in compiling will have an impact on their graduation. This is a serious problem faced by educational institutions. Out of 328 students at PTIK FKIP UNS who took thesis credits, only 85 were able to graduate on time. Therefore, this study was conducted to identify the causes. The research data was taken from the alumnus class of 2012 to 2017. The data was processed using RapidMiner software. The technique used was the decision tree classification technique with the C4.5 algorithm, and to optimize the accuracy of the model, the Particle Swarm Optimization (PSO) algorithm was also added. This study got an accuracy rate of 76% and an AUC score of 0.733.</span>

Early assessment of the risk of child welfare involvement for preventive purposes

BackgroundTo prevent child welfare involvement by timely referring families to preventive interventions, it is essential to assess the risk of child welfare involvement in the general population as early as possible. ObjectiveTo examine whether child welfare involvement can be predicted by demographic, socioeconomic, and criminal history factors. Participants and settingData of 131,532 children and their parents were retrieved from Statistics Netherlands. Child welfare involvement comprised supervision orders, court-imposed guardianship, and/or out-of-home placements, that started in the year following the assessment of risk factors. MethodsAUC values were calculated to establish the predictive validity of individual factors for future child welfare involvement. A decision tree classification technique with a split-sample validation approach was used to develop and validate a predictive model. ResultsAn accumulation of risk factors proved to be highly predictive of child welfare involvement in the year following the assessment of the risk factors. The risk increased exponentially as the number of risk factors increases. The presence of four or more risk factors yielded a 10 times higher risk for child welfare involvement, and the presence of six or more risk factors yielded a 21 times higher risk relative to the absence of risk factors. The predictive model showed that above an accumulation of risk factors, the presence of specific risk factors further increased the risk. ConclusionsBoth the predictive model and the mere accumulation of risk factors can help professionals to estimate the risk of future child welfare involvement, which is important for timely referring families to preventive interventions or assistance.

Secure and Sustainable Framework for Cattle Recognition Using Wireless Multimedia Networks and Machine Learning Techniques

Towards the successful operation of any farm, effective livestock management is necessary. The efficiency, affordability, and scalability of management solutions play a crucial role in modern farming. In this paper, a novel low-cost framework is proposed to recognize for health monitoring of individual cattle based on their sensed behavioral activity data and muzzle (nose) pattern image database using incremental decision tree classification techniques and accelerometer-based activity monitoring method. The proposed system performs image prepossessing on the captured muzzle point images of cattle to mitigate the noise, enhance the contrast, and increase quality. We extract the minutiae features to improve the system’s accuracy for recognizing the cattle for accurate and early classification of behavioral activity for health monitoring. The proposed system uses a support vector machine and incremental decision tree classifiers to classify the extracted feature of cattle’s muzzle images and health database. The server has encrypted databases that consist of captured muzzle images, owner database, health sensor database provided by the owners and systems. We use a similarity score measurement using incremental decision tree classification for matching and classify the muzzle point featutes with the database for cattle identification and health monitoring. We also developed a prototype for evaluating the accuracy of the proposed system with 97.99% accuarcy for unique identification of individual cattle.

Detection of Cardiovascular Disease Using Ensemble Feature Engineering With Decision Tree

Cardiovascular diseases are a cluster of heart-related issues, including many comorbidities, which are becoming a leading cause of human death across the globe. Hence, an essential framework is demanded for the early detection of CVDs which can help to prevent premature death. The application of Artificial Intelligence (AI) in healthcare has opted for this challenge and makes it easier to detect CVDs using a computational model. In this study, the authors built a reduced dataset using ensemble feature selection methods and got five features as per their weight values. Support Vector Machine, Logistic Regression, and Decision Tree classification techniques are utilized to check the effectiveness of newly designed datasets through different validation approaches. The authors also worked on data processing and visualization techniques, including Principal Component Analysis (PCA), and T-sne for understanding the data structure. From the findings, it was possible to conclude that DT has achieved an optimal accuracy and AUC of 98.9% and 0.99 ROC with leave one out Cross Validation (CV).

Retraction Note to: Automatic classification of emotions in news articles through ensemble decision tree classification techniques

Retraction Note to: Automatic classification of emotions in news articles through ensemble decision tree classification techniques

A new ML-based approach to enhance student engagement in online environment.

The educational research is increasingly emphasizing the potential of student engagement and its impact on performance, retention and persistence. This construct has emerged as an important paradigm in the higher education field for many decades. However, evaluating and predicting the student’s engagement level in an online environment remains a challenge. The purpose of this study is to suggest an intelligent predictive system that predicts the student’s engagement level and then provides the students with feedback to enhance their motivation and dedication. Three categories of students are defined depending on their engagement level (Not Engaged, Passively Engaged, and Actively Engaged). We applied three different machine-learning algorithms, namely Decision Tree, Support Vector Machine and Artificial Neural Network, to students’ activities recorded in Learning Management System reports. The results demonstrate that machine learning algorithms could predict the student’s engagement level. In addition, according to the performance metrics of the different algorithms, the Artificial Neural Network has a greater accuracy rate (85%) compared to the Support Vector Machine (80%) and Decision Tree (75%) classification techniques. Based on these results, the intelligent predictive system sends feedback to the students and alerts the instructor once a student’s engagement level decreases. The instructor can identify the students’ difficulties during the course and motivate them through e-mail reminders, course messages, or scheduling an online meeting.

Embedding Quality into Software Product Line Variability Artifacts

The success of any software product line development project is closely tied to its domain variability management. Whereas a lot of effort has been put into functional variability management by the SPL community, non-functional variability is considered implicit. The result has been dissatisfaction among clients due to resultant poor quality systems. This work presents an integrated requirement specification template for quality and functional requirements at software product line variation points. The implementation of this approach at the analytical description phase increases the visibility of quality requirements obliging developers to implement them. The approach proposes the use of decision tree classification techniques to support the weaving of functional quality attributes at respective variation points. This work, therefore, promotes software product line variability management objectives by proposing new functional quality artifacts during requirements specification phase. The approach is illustrated with an exemplar mobile phone family data storage requirements case study.

A Hybrid Model to Classify Patients with Chronic Obstructive Respiratory Diseases.

Over the last decades, an increase in the ageing population and age-related diseases has been observed, with the increase in healthcare costs. As so, new solutions to provide more efficient and affordable support to this group of patients are needed. Such solutions should never discard the user and instead should focus on promoting more healthy lifestyles and provide tools for patients’ active participation in the treatment and management of their diseases. In this concern, the Personal Health Empowerment (PHE) project presented in this paper aims to empower patients to monitor and improve their health, using personal data and technology assisted coaching. The work described in this paper focuses on defining an approach for user modelling on patients with chronic obstructive respiratory diseases using a hybrid modelling approach to identify different groups of users. A classification model with 90.4% prediction accuracy was generated combining agglomerative hierarchical clustering and decision tree classification techniques. Furthermore, this model identified 5 clusters which describe characteristics of 5 different types of users according to 7 generated rules. With the modelling approach defined in this study, a personalized coaching solution will be built considering patients with different necessities and capabilities and adapting the support provided, enabling the recognition of early signs of exacerbations and objective self-monitoring and treatment of the disease. The novel factor of this approach resides in the possibility to integrate personalized coaching technologies adapted to each kind of user within a smartphone-based application resulting in a reliable and affordable alternative for patients to manage their disease.

Embedding Quality into Software Product Line Variability Artifacts

The success of any software product line development project is closely tied to its domain variability management. Whereas a lot of effort has been put into functional variability management by the SPL community, non-functional variability is considered implicit. The result has been dissatisfaction among clients due to resultant poor quality systems. This work presents an integrated requirement specification template for quality and functional requirements at software product line variation points. The implementation of this approach at the analytical description phase increases the visibility of quality requirements obliging developers to implement them. The approach proposes the use of decision tree classification techniques to support the weaving of functional quality attributes at respective variation points. This work, therefore, promotes software product line variability management objectives by proposing new functional quality artifacts during requirements specification phase. The approach is illustrated with an exemplar mobile phone family data storage requirements case study.

Smart Cardio Forecasting System for Patients with Cardiovascular Diseases Who Live Alone

The widespread use of smartwatches has increased their specific and complementary activities in the health sector for patient’s prognosis. In this study, we propose a framework referred to as smart forecasting CardioWatch (SCW) to measure the heart-rate variation (HRV) for patients with myocardial infarction (MI) who live alone or are outside their homes. In this study, HRV is used as a vital alarming sign for patients with MI. The performance of the proposed framework is measured using machine learning and deep learning techniques, namely, support vector machine, logistic regression, and decision-tree classification techniques. The results indicated that the analysis of heart rate can help health services that are located remotely from the patient to render timely emergency health care. Further, taking more cardiac parameters into account can lead to more accurate results. On the basis of our findings, we recommend the development of health-related software to aid researchers to develop frameworks, such as SCW, for effective provision of emergency health.

Early detection of breast cancer using hybrid of series network and VGG-16

Breast cancer is nowadays becoming a serious problem and acts as a main reason for death of women around the world. Hence various devices are being utilized for the detection of breast cancer at an earlier stage and diagnosing it in an earlier stage might even results in complete cure of the disease. Among the wide range of devices available, mammogram is one of the commonly employed and most effective approaches involved in the detection of breast cancer. It records the affected area in the form of mammogram images and these images are processed through image processing techniques for the detection of cancer affected regions. In this paper, novelties have given in all the image processing aspects such as filtering, segmentation, feature extraction and classification. The salt and pepper noises in the mammogram images are eliminated by the usage of novel decision based partial median filter. Then the filtered images are segmented based utilizing a novel technique which is formed on integrating the deep learning techniques of VGG-16 and series network. Features of the segmented images have extracted through BAT-SURF feature extraction, where the orientation of the interest points are extracted using Bat optimization algorithm along with SURF (i.e.) Speeded up Robust Features. It extract most important key points from SURF features and then the extracted image has classified by using the novel Gradient descent decision tree classifier in which a stable learning path provided for easy convergence. Then the performance of the proposed system has analyzed based on the performance metrics like accuracy, specificity, sensitivity, recall, precision, Jaccard coefficient, F score and missed classification. Based on the results obtained, the conclusion of the proposed work has attained enhanced results on comparing with other state of the art approaches. The accuracy value of the proposed hybrid VGG-16 and series network segmentation technique determined as 96.45 and similarly the accuracy value of the proposed Gradient Descent Decision Tree Classification technique has value shows 95.15.

Predicting Business Innovation Intention Based on Perceived Barriers: A Machine Learning Approach

In the Industry 4.0 scenario, innovation emerges as a clear driver for the economic development of societies. This effect is particularly true for the least developed countries. Nevertheless, there is a lack of studies that analyze this phenomenon in these nations. In this context, this study aims to examine the impact of perceived barriers to innovation to predict companies′ innovative intentions in an emerging economy. This study is a preliminary effort to use data mining and symmetry-based learning concepts, especially classification, to assist the identification of strategies to incentivize intention to innovate in companies. Using the decision tree classification technique, we analyzed a sample of Chilean companies (N = 5876). The sample was divided into large enterprises (LEs) and small and medium enterprises (SMEs). In the group of large companies, the barriers that most impact the intention to innovate are innovation cost, lack of demand innovations, and lack of qualified personnel. Alternatively, in the group of small-medium companies, the barriers that most impact the intention to innovate are lack of own funds, lack of demand innovations, and lack of information about technology. These results show how the perceptions of barriers are significant to predict the intentions of innovation in Chilean companies. Furthermore, the perceptions of these barriers are contingent on the organizational sizes. These findings contribute to understanding the effect of contingencies on innovative intention in an emerging economy.

RETRACTED ARTICLE: Automatic classification of emotions in news articles through ensemble decision tree classification techniques

Emotions form a major role in human life. As human interactions with online systems have increased drastically, emotion prediction from online text, which otherwise can be monotonous, would help to provide a better environment to the users. Identification of emotions from a normal text itself is very complicated while news text that does not explicitly convey emotions adds more intricacy to it. Data mining methods can be utilized in this context. In this work, the potential of decision tree classifiers in emotion classification is explored. The advocated methodology incorporates two segments towards emotion identification. The first segment deals with data preparation and involves dataset elicitation, translation, HTML tag removal, stop word elimination and stemming. The second segment that implements data mining takes the output of the first segment as its input and applies feature vector formulation, correlation based feature selection, building of bagged Grafted C4.5 learning model and performance evaluation. Based on the evolved classification rules, the emotions are categorized into joy, surprise, fear, sadness, disgust, neutral and mixed kind. Experiments have been conducted to analyse the effect of feature selection methods and ensemble methods in generating efficient rules. The accuracy is compared against eight other decision tree classifiers and also the support vector machine learning model. The proposed methodology achieves the maximum accuracy of 87.83% justifying its utilization in the real time applications.

Evaluation of Classification Techniques for Land Use Change Mapping of Indian Cities

This study looks into the development of multi-level classification approach for land use change mapping in Indian cities using Landsat imageries. In this study, we mapped 47 Indian cities at different time frames 1990, 2000, 2010, and 2017. We started with traditional classification methods, but results provided unsatisfactory accuracy levels. Thus, we employed multiple classification techniques to achieve results with higher accuracy. The paper captures the evaluation of different classification techniques—hybrid, unsupervised, decision tree classification (DTC), and object-based image analysis (OBIA). The results suggest improvement in accuracy levels by using multi-level classification for different cities at different stages of the classification process. The most prominent is the hybrid classification technique; 14 cities out of 47 reached to accuracy above 72% through hybrid classification. For problematic classes, we used DTC, OBIA, and unsupervised classification techniques after masking the datasets. DTC was used in cities with a greater number of problems in datasets. For example, in the case of Kochi City, the accuracy at the initial level was reported 51% through unsupervised classification which improved to 77% (supervised classification), and finally, it reached 90% by DTC technique. The overall accuracy achieved through the multi-level classification approach described in this paper for the 47 Indian cities ranges from 81 to 93%.

Spare part supplier selection model using decision tree classification techniques: J48 Algorithm

Spare parts are goods that consist of several components that form a single unit and have certain functions. To facilitate companies in selecting suppliers, a supplier selection model is needed to make it easier for companies to select suppliers and make it easier to see strategic directions to take several criteria from suppliers to achieve priorities. This research was conducted at a manufacturing company engaged in car spare parts with rubber raw materials. In this study the problems that occur are the difficulty of the company in selecting suppliers based on criteria that are in accordance with the company and the delay in the receipt of raw materials, and the lack of raw materials supplied from each supplier. This study aims to classify suppliers based on desired criteria by the company and to design a supplier selection model in the long term. The J48 algorithm is produced by a supplier selection model with 2 rule selection models to classify efficient suppliers and inefficient suppliers. The accuracy of the Decision Tree model is 90.8547%, the MAE error value is 0.1256. From the J48 algorithm the biggest gain is the criteria for quality, price, delivery, and warranty and complaint services.