Decision Tree Classification Algorithm Research Articles

Improving the Efficiency by Novel Feature Extraction Technique Using Decision Tree Algorithm Comparing with SVM Classifier Algorithm for Predicting Heart Disease

Aim: The objective of the research work is to use the two machine learning algorithms Decision Tree(DT) and Support vector machine(SVM) for detection of heart disease on earlier stages and give more accurate prediction. Materials and methods: Prediction of heart disease is performed using two machine learning classifier algorithms namely, Decision Tree and Support Vector Machine methods. Decision tree is the predictive modeling approach used in machine learning, it is a type of supervised machine learning. Support-vector machines are directed learning models with related learning calculations that break down information for order and relapse investigation. The significance value for calculating Accuracy was found to be 0.005. Result and discussion: During the process of testing 10 iterations have been taken for each of the classification algorithms respectively. The experimental results shows that the decision tree algorithm with mean accuracy of 80.257% is compared with the SVM classifier algorithm of mean accuracy 75.337% Conclusion: Based on the results achieved the Decision Tree classification algorithm better prediction of heart disease than the SVM classifier algorithm.

Sentiment Analysis About Indonesian Lawyers Club Television Program Using K-Nearest Neighbor, Naïve Bayes Classifier, And Decision Tree

Indonesia Lawyers Club (ILC) is a talk show on TVOne that discusses topics around public phenomena, legal issues, crime, and other similar topics. In 2018, ILC won the Panasonic Gobel Awards as the best news talk show program. But in 2019, ILC failed to win the award which was won by Mata Najwa which featured a talk show event that appeared on Trans7. As one of the television shows that has won awards, ILC has pros and cons for its shows from the public. This study applies a sentiment analysis approach to examine public opinion on Twitter about Mata Najwa and ILC in 2018 and 2019. This study applies K-Nearest Neighbor, Naïve Bayes Classifier, and Decision Tree classification algorithm to validate the result. The contribution of this study is to show that public opinion on Twitter can be examined to figure out community sentiment on a tv talk show as well as to confirm the Award winner of tv Talkshow.
 
 Index Terms—datamining; Decision Tree; K-NN; Naïve Bayes Classifier; sentiment analysis

Design and Development of an Algorithm to Detect and diagnose Parkinson’s disease

Parkinson’s disease is a neurological disorder that affects the neurons that produce a chemical substance known as dopamine. The symptoms of this disease are tremors, stiffness all around the body, vocal impairment. If not treated early this disease might even lead to death. This disease doesn’t have a treatment, but an early prediction might help in the reduction of the progress of the disease. Detecting the disease is much more difficult as there is no quantitative test that can be conducted to detect the disease. Voice is one of the primary symptoms of this disease and therefore the features present in the voice can be extracted and can be used to train a model to detect whether the person is suffering from the disease or not. An ensemble learning voting classifier algorithm is designed with a performance accuracy of more than ninety percent and is used for the prediction of the disease using the vocal features extracted from the person. This algorithm is trained with the dataset which contains both normal as well as the affected person’s voice features. Decision tree classifier algorithm, Logistic Regression, and that of the Support Vector Machine Algorithm are used as the input for the voting classifier which is used for detecting the disease.

Serum protein triplet TGF-\u03b21, TIMP-1, and YKL-40 serve as diagnostic and prognostic profile for astrocytoma

Astrocytoma is the most common glial tumour of the CNS. The most malignant form is grade IV Astrocytoma, also called Glioblastoma. Due to its heterogeneity, aggressiveness and lethal nature scientists are trying to find less invasive methods for early prediction of tumour onset, recurrence, response to therapy and patients’ survival. Here, applying decision tree classification algorithm we performed astrocytoma specific protein profile analysis on serum proteins TIMP-1, active and latent form of TGF-β1, IP-10, ANGPT-1, OPN, and YKL-40 using enzyme-linked immunosorbent detection assay (ELISA). Results have demonstrated that astrocytoma specific profile consisted of three proteins—active form of TGF-β1, TIMP-1 and YKL-40 and was able to correctly classify 78.0% (103/132) of sample and 83.3% (60/72) of astrocytoma sample. Calculating decision tree algorithm associated with astrocytoma patient survival, prediction model reached an accuracy of 83.3% (60/72). All together these results indicate that glioma detection and prediction from patient serum using glioma associated proteins and applying mathematical classification tools could be achieved, and applying more comprehensive research further could be implemented in clinic.

Data Classification Using Decision Trees J48 Algorithm for Text Mining of Business Data

The business industry is generating a lot of data on daily business deals and financial transactions. These businesses are generating intensive-data like they need customer satisfaction on top priority, fulfilling their needs, etc. In every step, Data is being produced. This Data has a great value that is hidden from regular users. Data analytics is used to unhide those values. In our project, we are using a business-related dataset that contains strings and their class (0 or 1). 0 or 1 denotes the positive or negative string labels. To analyze this data, we are using a decision tree classification algorithm (J48 exceptionally) to perform text mining (classification) on our target dataset. Text mining comes under supervised learning (type). In-text mining, generally, we use two datasets. One is used to train the model, and the second dataset is used to predict the missing class labels in the second dataset based on this training model generated using the first dataset.

A Proposed Model for Predicting Employee Turnover of Information Technology Specialists Using Data Mining Techniques

This article proposes a data mining framework to predict the significant explanations of employee turn-over problems. Using Support vector machine, decision tree, deep learning, random forest, and other classification algorithms, the authors propose features prediction framework to determine the influencing factors of employee turn-over problem. The proposed framework categorizes a set of historical behavior such as years at company, over time, performance rating, years since last promotion, and total working years. The proposed framework also classifies demographics features such as Age, Monthly Income, and Distance from Home, Marital Status, Education, and Gender. It also uses attitudinal employee characteristics to determine the reasons for employee turnover in the information technology sector. It has been found that the monthly rate, overtime, and employee age are the most significant factors which cause employee turnover.

Feature based classification of voice based biometric data through Machine learning algorithm

In the era of big data and growing artificial intelligence, the requirement and necessity of biometric identification increase in a rapid manner. The digitalization and recent Pandemic crisis gives a boost to need to authorized identification which get fulfilled with biometric identification. Our paper focuses on same concept of checking the identification accuracy of machine learning algorithm REPTree on selected biometric dataset which is being deployed and evaluated on a data mining tool WEKA. Our target is to achieve more or equal to 95 percentages in order to predict the given sample data is accurately classified into our target variables values i.e. male female. The selected algorithm REPTree is a kind of decision tree classification algorithm which works on same concept as C4.5 and decision tree algorithm with speciality of generation of both kind of output i.e. discrete and continuous. The selection of algorithm gives us benefits with achievement of higher accuracy and selection of dataset also become easy with some required modification and pre-processing of data with some dimension reduction filters.

Human activity classification using Decision Tree and Naïve Bayes classifiers

With rapid development in wireless sensor networks and continuous improvements in developing artificial intelligence-based scientific solutions, the concept of ambient assisted living has been encouraged and adopted. This is due to its widespread applications in smart homes and healthcare. In this regard, the concept of human activity recognition (HAR) & classification has drawn numerous researchers’ attention as it improves the quality of life. However, before using this concept in real-time scenarios, it is required to analyse its performance following activities of daily living using benchmarked data set. In this continuation, this work has adopted the activity classification algorithms to improve their accuracy further. These algorithms can be used as a benchmark to analyse others’ performance. Initially, the raw 3-axis accelerometer data is first preprocessed to remove noise and make it feasible for training and classification. For this purpose, the sliding window algorithm, linear and Gaussian filters have been applied to raw data. Then Naive Bayes (NB) and Decision Tree (DT) classification algorithms are used to classify human activities such as: sitting, standing, walking, sitting down and standing up. From results, it can be seen that maximum 89.5% and 99.9% accuracies are achieved using NB and DT classifiers with Gaussian filter. Furthermore, we have also compared the obtained results with its counterpart algorithms in order to prove its effectiveness.

A New Multi-Stage Feature Selection and Classification Approach: Bank Customer Credit Risk Scoring

Abstract Lots of information about customers are stored in the databases of banks. These databases can be used to assess the credit risk. Feature selection is a well-known concept to reduce the dimension of such databases. In this paper, a multi-stage feature selection approach is proposed to reduce the dimension of database of an Iranian bank including 50 features. The first stage of this paper is devoted to removal of correlated features. The second stage of it is allocated to select the important features with genetic algorithm. The third stage is proposed to weight the variables using different filtering methods. The fourth stage selects feature through clustering algorithm. Finally, selected features are entered into the K-nearest neighbor (K-NN) and Decision Tree (DT) classification algorithms. The aim of the paper is to predict the likelihood of risk for each customer based on effective and optimum subset of features available from the customers.

Comparative analysis of machine learning algorithms for prediction of smart grid stability †

The global demand for electricity has visualized high growth with the rapid growth in population and economy. It thus becomes necessary to efficiently distribute electricity to households and industries in order to reduce power loss. Smart Grids (SG) have the potential to reduce such power losses during power distribution. Machine learning and artificial intelligence techniques have been successfully implemented on SGs to achieve enhanced accuracy in customer demand prediction. There exists a dire need to analyze and evaluate the various machine learning algorithms, thereby identify the most suitable one to be applied to SGs. In the present work, several state-of-the-art machine learning algorithms, namely Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Logistic Regression, Naive Bayes, Neural Networks, and Decision Tree classifier, have been deployed for predicting the stability of the SG. The SG dataset used in the study is publicly available collected from UC Irvine (UCI) machine learning repository. The experimentation results highlighted the superiority of the Decision Tree classification algorithm, which outperformed the other state of the art algorithms yielding 100% precision, 99.9% recall, 100% F1 score, and 99.96% accuracy.

A genetic algorithm approach for predicting ribonucleic acid sequencing data classification using KNN and decision tree

Malaria larvae accept explosive variable lifecycle as they spread across numerous mosquito vector stratosphere. Transcriptomes arise in thousands of diverse parasites. Ribonucleic acid sequencing (RNA-seq) is a prevalent gene expression that has led to enhanced understanding of genetic queries. RNA-seq tests transcript of gene expression, and provides methodological enhancements to machine learning procedures. Researchers have proposed several methods in evaluating and learning biological data. Genetic algorithm (GA) as a feature selection process is used in this study to fetch relevant information from the RNA-Seq Mosquito Anopheles gambiae malaria vector dataset, and evaluates the results using kth nearest neighbor (KNN) and decision tree classification algorithms. The experimental results obtained a classification accuracy of 88.3 and 98.3 percents respectively.

Fraud Detection in Customers’ Electricity Consumption in Nigeria Using Support Vector Machine and C4.5 Decision Tree Algorithms

Over the years, Electricity theft has been estimated to cost billions of Naira per year in Nigeria. To reduce electricity theft, electric utilities are leveraging data collected by using data analytics to identify abnormal consumption trends and possible fraud. In this study, use of data analytics in detecting electricity theft, and a metric that leverages this threat model in order to evaluate and compare anomaly detectors. Data mining technology have helped several industries and sectors in improving their various forms of technology, this study therefore employ machine learning algorithms for the classification of fraud detection in the electricity consumption of costumers. In this project, Support Vector Machine (SVM) and C4.5 Decision tree classification algorithms were employed for fraud detection using customer electricity consumption data. SVM and C4.5 achieved 63.4% and 65.9% accuracy respectively. Thus, C4.5 outperformed SVM based on the dataset experimented in this project. Keywords: Active databases, distributed processing, intelligent processing, optimization, query processor

The Design Model for Robotic Waitress

With the rapid development of traditional industries, intelligent robots have been widely used in the hospitality industry. Although the development of intelligent robots faces a positive trend and a good market in the hospitality industry, it also faces the problem that robots cannot effectively collect and use user data in the field of human–computer interaction. It not only affects the interaction experience between users and robots, but also prevents companies from getting valuable feedback in a timely manner. In order for intelligent robots to effectively utilize interactive information, the user experience of robot entertainment is improved. This paper proposes and establishes a basic technical model called iRCXM. Combining the iRCXM model with a decision tree classification algorithm is excepted effectively improve the interaction experience between humans and robots in hospitality. This paper designs a model of intelligent robot based on decision tree algorithm. The model divides the user into three sections, each corresponding to a different standard function. Using a decision tree classification algorithm model is excepted effectively judge users’ current stage and whether they can move to the next stage. When the user reaches the final stage, it proves that the user has obtained a good interactive experience. At the same time, for users at different stages, the model will provide strategies for downward transformation so that companies can adjust and improve existing problems in a timely manner. In addition, the research developed a robot user interaction system based on the existing technology. The system is based on Android. Using HTTP protocol and Baidu Cloud AI API to realize simple face recognition and Sanbot-OpenSDK to implement simple robot control, the development of this system is to verify the feasibility of the model. The developed samples were tested in a real environment and feedback from customer experience was collected through semi-structured interviews. Finally, the feasibility of the model is verified.

Dynamic traffic classification algorithm and simulation of energy Internet of things based on machine learning

With the rapid development of information technology, a large amount of traffic generated by various Internet applications occupies a large amount of network resources. It poses a huge challenge to service quality and has a negative impact on Internet security. In order to utilize network resources effectively and provide effective management and control measures for network administrators, network traffic classification technologies is a hot topic for scientists to identify application layer protocols. Today, there are more and more applications based on TCP/IP. With the emergence of various anti-surveillance applications, traditional port and application-based identification methods are difficult to meet current or future traffic identification requirements. It has become a very challenging problem to require more efficient, accurate, intelligent and real-time Internet traffic identification. The Internet of Things is a new network concept proposed by people who based on Internet prototypes. It enables the end user of the system can carry out communication and exchange of information and data between any project. In recent years, with the continuous advancement of Internet of Things technology, the coverage of the Internet of Things has become very wide, and the number of different types of networks that make up the Internet of Things is also increasing. This paper aims to find the dynamic network traffic classification problem of hybrid fixed in dynamic network and dynamic network in mobile network, and gives a reasonable mapping scheme. The dynamics of network traffic for Internet of Things are reflected fully and will not cause route flapping. The simulation results show that the decision tree classification algorithm in machine learning has higher efficiency, and improves the utilization of network resources.

Clinical impacts of the mutational spectrum in Japanese patients with primary myelofibrosis.

Patients with primary myelofibrosis (PMF) have a poorer prognosis than those with other subtypes of myeloproliferative neoplasms (MPNs). To investigate the relationship between gene mutations and the prognosis of Japanese PMF patients, we analyzed mutations in 72 regions located in 14 MPN-relevant genes (CSF3R, MPL, JAK2, CALR, DNMT3A, TET2, EZH2, ASXL1, IDH1/2, SRSF2, SF3B1, U2AF1, and TP53) utilizing a target resequencing platform. In our cohort, ASXL1 mutations were more frequently detected in both overt and prefibrotic PMF patients than other mutations. The frequency of ASXL1 mutations was slightly higher among overt PMF patients than among prefibrotic PMF patients (44.6% vs 25.0%, FDR= 0.472). Decision tree classification algorithms revealed that ASXL1, EZH2, and SRSF2 mutations were associated with a poor prognosis for overt PMF. Overall survival was significantly shorter in patients harboring ASXL1, EZH2, or SRSF2 mutations than in those without these mutations (p = 0.03). These results suggest that, as reported in Western countries, MIPSS70 is applicable to Japanese PMF patients and ASXL1, EZH2, and SRSF2 mutations may be utilized as surrogate markers of a poor prognosis.

Improved differentiation classification of variable precision artificial intelligence higher education management

The wide application of artificial intelligence technology in various fields has accelerated the pace of people exploring the hidden information behind large amounts of data. People hope to use data mining methods to conduct effective research on higher education management, and decision tree classification algorithm as a data analysis method in data mining technology, high-precision classification accuracy, intuitive decision results, and high generalization ability make it become a more ideal method of higher education management. Aiming at the sensitivity of data processing and decision tree classification to noisy data, this paper proposes corresponding improvements, and proposes a variable precision rough set attribute selection standard based on scale function, which considers both the weighted approximation accuracy and attribute value of the attribute. The number improves the anti-interference ability of noise data, reduces the bias in attribute selection, and improves the classification accuracy. At the same time, the suppression factor threshold, support and confidence are introduced in the tree pre-pruning process, which simplifies the tree structure. The comparative experiments on standard data sets show that the improved algorithm proposed in this paper is better than other decision tree algorithms and can effectively realize the differentiated classification of higher education management.

A class skew-insensitive ACO-based decision tree algorithm for imbalanced data sets

<span>Ant-tree-miner (ATM) has an advantage over the conventional decision tree algorithm in terms of feature selection. However, real world applications commonly involved imbalanced class problem where the classes have different importance. This condition impeded the entropy-based heuristic of existing ATM algorithm to develop effective decision boundaries due to its biasness towards the dominant class. Consequently, the induced decision trees are dominated by the majority class which lack in predictive ability on the rare class. This study proposed an enhanced algorithm called hellinger-ant-tree-miner (HATM) which is inspired by ant colony optimization (ACO) metaheuristic for imbalanced learning using decision tree classification algorithm. The proposed algorithm was compared to the existing algorithm, ATM in nine (9) publicly available imbalanced data sets. Simulation study reveals the superiority of HATM when the sample size increases with skewed class (Imbalanced Ratio < 50%). Experimental results demonstrate the performance of the existing algorithm measured by BACC has been improved due to the class skew-insensitiveness of hellinger distance. The statistical significance test shows that HATM has higher mean BACC score than ATM.</span>

A Novel Financial Risk Identification Algorithm for Online Fintech Platform

The issues we study are risk identification, credit granting, pre-loan and post-loan lending risks in consumer lending and trust loans. How to effectively evaluate and identify the potential default risk of borrowers and calculate the probability of default of borrowers before granting loans is a fundamental part of credit risk management in modern financial institutions. This paper focuses on the statistical analysis of historical loan data from banks and other financial institutions with the help of the idea of non-equilibrium data classification, and the use of the random forest algorithm to establish a loan default prediction model. Then a fixed threshold screening in the form of a black and white list is used. The limitation of the current method is that it cannot meet the lending needs of large-scale transaction classes and cannot be judged quickly. When the number of decision trees in a random forest is large, the space and time required for training are large. In addition, it is insensitive to data and has low accuracy. The experiments found that the machine learning-based financial lending risk prediction method can solve the problem better, and the experimental results phenotyped that the neural network and random forest algorithm outperformed the decision tree and logistic regression classification algorithm in prediction performance. In addition, by using the random forest algorithm to rank the importance of features, the features that have a greater impact on whether the final default can be obtained, so that the lending risk judgment in the financial sector can be more effective.

Financial Credit Default Forecast Based on Big Data Analysis

How to effectively evaluate and identify the potential default risk of borrowers and calculate the default probability of borrowers before issuing loans is the basis and important link of the credit risk management of modern financial institutions. This paper mainly studies the statistical analysis of historical loan data of banks and other financial institutions with the help of the idea of non-balanced data classification, and uses machine learning algorithms (not statistical algorithms) such as random forest, logical regression and decision tree to establish loan default prediction model. The experimental results show that neural network and random forest algorithm outperform decision tree and logistic regression classification algorithm in prediction performance. In addition, by using the random forest algorithm to rank the importance of features, the features that have a greater impact on the final default can be obtained, so as to make a more effective judgment on the loan risk in the financial field.

A Contemporary Machine Learning Method for Accurate Prediction of Cervical Cancer

With the advent of new technologies in the medical field, huge amounts of cancerous data have been collected and are readily accessible to the medical research community. Over the years, researchers have employed advanced data mining and machine learning techniques to develop better models that can analyze datasets to extract the conceived patterns, ideas, and hidden knowledge. The mined information can be used as a support in decision making for diagnostic processes. These techniques, while being able to predict future outcomes of certain diseases effectively, can discover and identify patterns and relationships between them from complex datasets. In this research, a predictive model for predicting the outcome of patients’ cervical cancer results has been developed, given risk patterns from individual medical records and preliminary screening tests. This work presents a Decision tree (DT) classification algorithm and shows the advantage of feature selection approaches in the prediction of cervical cancer using recursive feature elimination technique for dimensionality reduction for improving the accuracy, sensitivity, and specificity of the model. The dataset employed here suffers from missing values and is highly imbalanced. Therefore, a combination of under and oversampling techniques called SMOTETomek was employed. A comparative analysis of the proposed model has been performed to show the effectiveness of feature selection and class imbalance based on the classifier’s accuracy, sensitivity, and specificity. The DT with the selected features and SMOTETomek has better results with an accuracy of 98%, sensitivity of 100%, and specificity of 97%. Decision Tree classifier is shown to have excellent performance in handling classification assignment when the features are reduced, and the problem of imbalance class is addressed.