Implementation Of Decision Tree Research Articles

Application of Cross-Validation Techniques to Handle Overfitting in a Case Study of Decision Tree Implementation for Lung Cancer Prediction

Lung cancer is a condition caused by cancer cells growing in the lungs. Lung cancer causes a weakened immune system, tumors, and other abnormalities that prevent the body from functioning properly. Lung cancer examination uses various technologies, namely CT Scan, X-ray, and others. However, the examination is relatively expensive and takes a long time. The use of machine learning makes it possible to support lung cancer diagnosis. With the large amount of medical data available today, machine learning can recognize patterns in the data so that it will help the process of diagnosing lung cancer more effectively. This study aims to correct overfitting in previous research which used the decision tree method to predict lung cancer with cross-validation techniques. In this research, we use a public dataset from Data World. This dataset consists of 25 data attributes and has 1000 data. The results of this research are rules obtained from decision trees which are then evaluated to produce 96.7% accuracy, 96.7% precision, 96.7% recall, and 96.7% f1-score. These results show that the decision tree method performs well in predicting lung cancer early and the cross-validation technique can overcome overfitting in decision trees with more general and stable results.

Implementasi Decision Tree untuk Prediksi Kelahiran Bayi Prematur

The early birth of baby in Indonesia is a case that has a very high incidence rate. According to data from the Ministry of Health in 2021, the presentation of premature babies in Indonesia is 84%. The number of infant deaths in Indonesia is still relatively high compared to other ASEAN countries. The purpose of this study was to predict the birth of premature babies with the implementation of decision tree, with this type of predictive analysis research. The population in this study is pregnant women patients with a sample of 350 pregnant women patient data covering the variables studied Age, BMI, Vaginal Discharge, History of Miscarriage, History of Prematurity and Pregnancy Spacing. The prediction was made by halving the training data by 245 and the testing data by 105. The results obtained are the variable Body Mass Index (BMI) is the riskiest factor for premature birth The decision tree model yields an AUC of 91.7%, it can be concluded that the decision tree has a good classification accuracy value.

Data-Driven Decision Making in Scholarship Programs: Leveraging Decision Trees and Clustering Algorithms

Data-Driven Decision Making in Scholarship Programs: Leveraging Decision Trees and Clustering Algorithms

Implementasi Algoritma Data Mining J48 Untuk Klasifikasi Mahasiswa Yang Layak Mendapat Beasiswa PPA

The PPA Scholarship is a support for educational costs that is given to students who have taken at least semester 2 in Higher Education with consideration of limited economic capacity and have academic and non-academic achievements. In classifying students who are eligible for PPA scholarships, the management has difficulties because the limited PPA scholarships are not proportional to the number of students. One solution in classifying students who are eligible for PPA scholarships is to utilize the data of students who have received PPA scholarships in the previous year. However, in the classification process, data research is needed, one of the data research techniques is data mining. Where data mining is an interesting pattern extraction of large amounts of data [1]. One of the algorithms in data mining used in classification is the J48 algorithm. The J48 algorithm is the implementation of the C4.5 algorithm in the WEKA tools, besides the J48 algorithm is the decision tree implementation in the rapidminer tools. This algorithm is expected to help the management of the PPA scholarship at STMIK Budi Darma in classifying students who really deserve to receive the PPA scholarship. As a result, one of the criteria for a student to be classified as receiving a PPA scholarship is to have achievement

An Adaptive Gradient Boosting Model for the Prediction of Rainfall Using ID3 as a Base Estimator

While analyzing the data, it is crucial to choose the model that best matches the circumstance. Many experts in the field of classification and regression have proposed ensemble strategies for tabular data, as well as various approaches to classification and regression problems. In this paper, Gini Index is applied on raw geographical dataset to convert continuous data into discrete dataset. Decision tree algorithm is implemented on resultant discrete dataset, Information Gain is calculated for every attribute and the attribute with highest information gain is the splitting node, applied recursively. Decision tree algorithm implemented predicts the rainfall in Kashmir province with the accuracy of 81.5%. MDL pruning is applied on the resultant decision tree in order to reduce the size & complexity of the Decision tree. Pruning removes segments of the tree that contribute little towards classification; the accuracy is marginally reduced to 81.1%. Furthermore, after the implementation of Decision tree a boosting algorithm: gradient boosting has been implemented on the same set of data using decision tree as a base estimator. It was observed that the overall accuracy of the decision tree got increased to 87.5% after the implementation of gradient boosting model. Thus, the obtained results predict that gradient boosted-DT outperforms all other approaches with the highest accuracy measure and high susceptibility rate in rainfall prediction.

Implementation of Decision Tree for Making Decision of Claim Product from Steel Production

Product Claims is requests from consumers for products purchased from suppliers in accordance with agreements agreed by both parties. Products that have been claimed from consumers produce historical data sets that can be used as evaluations for producers to produce higher quality products. This study aims to process production data and shipment data then classify the types of products claimed based on the results of claim report from consumers. Data mining can be extracted information from a very large amount of data with specific methods to obtain information or new science. The method used in this study is the C4.5 algorithm method using the production code attribute as a claim or non-claim label attribute. This study produced a decision tree of 4 variables, there are thick of product, width of product, weight of product, destination of product, and type of product claim as label. This decision tree concept collects data which then calculates the value of entropy and gain to determine the rule. The conclusion from this study is the C4.5 algorithm helps classify the product claims and form a decision tree that can provide information about production results and can ensure with consumers related to product limits that may be claimed according to the agreed agreement. Evaluation of the results obtained that the algorithm C4.5 is 99.9% accuracy.

Fractional order differential calculus applied on decision making system to smart grid management via decision trees

This article portrays the relationship between fractional order differential calculus and the computational intelligence method, applying it to the improvement of intelligent systems. The Kirchhoff Laws, represented by second order differential equations, were solved via non-integer order differential calculus. The results obtained were used in the implementation of decision trees, which allowed the decision rules to be incorporated into the controllers. The results obtained by mathematical modeling did magnify the information extracted from Kirchhoff's Laws. Due to the gain magnitude of this information, the decision trees were obtained with greater precision and accuracy. In this way, it was achieved to build a hybrid system capable of being used in the development of controllers automata that has the lower response time and highest efficiency.

AutoProf – I. An automated non-parametric light profile pipeline for modern galaxy surveys

ABSTRACT We present an automated non-parametric light profile extraction pipeline called autoprof. All steps for extracting surface brightness (SB) profiles are included in autoprof, allowing streamlined analyses of galaxy images. autoprof improves upon previous non-parametric ellipse fitting implementations with fit-stabilization procedures adapted from machine learning techniques. Additional advanced analysis methods are included in the flexible pipeline for the extraction of alternative brightness profiles (along radial or axial slices), smooth axisymmetric models, and the implementation of decision trees for arbitrarily complex pipelines. Detailed comparisons with widely used photometry algorithms (photutils, xvista, and galfit) are also presented. These comparisons rely on a large collection of late-type galaxy images from the PROBES catalogue. The direct comparison of SB profiles shows that autoprof can reliably extract fainter isophotes than other methods on the same images, typically by >2 mag arcsec−2. Contrasting non-parametric elliptical isophote fitting with simple parametric models also shows that two-component fits (e.g. Sérsic plus exponential) are insufficient to describe late-type galaxies with high fidelity. It is established that elliptical isophote fitting, and in particular autoprof, is ideally suited for a broad range of automated isophotal analysis tasks. autoprof is freely available to the community at: https://github.com/ConnorStoneAstro/AutoProf.

Implementation of decision tree using C5.0 algorithm in preference and electability survey results on regional head election in Aceh

The decision tree is one of the methods of classification in data mining. There are many algorithms used to construct the tree model; one of them is C5.0 algorithm. The tree model with C5.0 algorithm was carried out based on the survey result dataset of the preference and electability of the regional head selection pre-campaign year 2018 in one of the districts in Aceh. The datasets consisted of 5 predictor variables, i.e. sub-districts, age, main occupations, highest education, and attracting factors from regional head candidate candidates. Variable categories of decisions ranged from candidates A, B, C, and D. The distribution of datasets was divided into training data and testing data using the k-fold cross-validation method. The optimum tree model formation was based on the accuracy value of model and coefficient of Kappa. The result showed that the best tree model was constructed using testing data on S = 10. The accuracy of the model and the Kappa coefficient were 0.8427 and 0.7208, respectively. There were three rules generated with five nodes. The main predictor variable contributing to the optimum model was the attracting factor of candidates and sub-districts.

Automated identification of bias inducing words in news articles using linguistic and context-oriented features

Media has a substantial impact on public perception of events, and, accordingly, the way media presents events can potentially alter the beliefs and views of the public. One of the ways in which bias in news articles can be introduced is by altering word choice. Such a form of bias is very challenging to identify automatically due to the high context-dependence and the lack of a large-scale gold-standard data set. In this paper, we present a prototypical yet robust and diverse data set for media bias research. It consists of 1,700 statements representing various media bias instances and contains labels for media bias identification on the word and sentence level. In contrast to existing research, our data incorporate background information on the participants’ demographics, political ideology, and their opinion about media in general. Based on our data, we also present a way to detect bias-inducing words in news articles automatically. Our approach is feature-oriented, which provides a strong descriptive and explanatory power compared to deep learning techniques. We identify and engineer various linguistic, lexical, and syntactic features that can potentially be media bias indicators. Our resource collection is the most complete within the media bias research area to the best of our knowledge. We evaluate all of our features in various combinations and retrieve their possible importance both for future research and for the task in general. We also evaluate various possible Machine Learning approaches with all of our features. XGBoost, a decision tree implementation, yields the best results. Our approach achieves anF1-score of 0.43, a precision of 0.29, a recall of 0.77, and a ROC AUC of 0.79, which outperforms current media bias detection methods based on features. We propose future improvements, discuss the perspectives of the feature-based approach and a combination of neural networks and deep learning with our current system.

Implementation of Decision Tree and Naïve Bayes Classification Method for Predicting Study Period

student learning performance influences the quality of a university. One indicator of assessment of student learning performance is the student’s study period. By predicting the student’s study period, universities can evaluate performance to strive to improve the quality of universities. Data mining is one of the choice technique in terms of predictions. But, each algorithm in data mining has the advantages of each, including Decision Tree and Naïve Bayes Algorithm which were tested in this research. This research shows Decision Tree is better than Naïve Bayes Algorithm to be the best choice to predicting study period with accuracy level tested for several ratios of training data and test data are 60:40,65:35,70:30,75:25 and 80:20 produce the highest accuracy values obtaines by decision tree with accuracy values are 90%, 89,14%, 89,3%, 88,8% and 88%. But, the amount of training data in this research does not affect the value of accuracy. This is shown from the value of accuracy in a certain ratio is very small but sometimes also the value of accuracy is high

The detection of Alternaria solani infection on tomatoes using ensemble learning

This paper presents a detection method of Alternaria solani in tomatoes. Several machine learning models were used to detect the pathogen, such as the implementation of decision trees and ensemble learning methods. The use of these methods requires the acquisition of large volumes of data and adequate preprocessing of this data. For the presented study the dataset of hyperspectral measurements of two varieties of tomatoes was used. Measurements were split into two groups: one inoculated with the Alternaria solani pathogen and the other one was treated as the reference. Measurements were taken by the spectroradiometer in consecutive measurement series. The main part of the study was the evaluation of the decision trees and the popular ensemble learning algorithms to select the most accurate one. After subsequent iterations of the training process and adjustment of hyperparameters, satisfactory accuracy results, equal to 0.987 for random forest, were obtained. This paper also covers the examination of the spectral range required for Alternaria solani identification. From several variants, the accuracy of models based on VIS and NIR spectral range was the closest to the accuracy obtained with the whole spectrum of measured absolute reflectance.

Implementation of Decision Trees as an Alternative for the Support in the Decisionmaking within an Intelligent System in Order to Automatize the Regulation of the Vocs in Non-Industrial Inside Environments

Implementation of Decision Trees as an Alternative for the Support in the Decisionmaking within an Intelligent System in Order to Automatize the Regulation of the Vocs in Non-Industrial Inside Environments

IMPLEMENTASI FUZZY DECISION TREE UNTUK PREDIKSI GAGAL GINJAL KRONIS

Fuzzy Decision Tree Implementation for Predicting Chronic Renal Failure. Kidney is one of the important organs for the body. The main function of kidney is for filtering process. The gradual decreasing of kidney function will lead to kidney disease and if it is left unchecked, it will lead to chronic renal failure. Chronic renal failure is a type of disease that can cause death. Until now there is no antidote for the disease of chronic renal failure, therefore this disease cannot be cured but its development can be slowed or stopped. The early diagnosis of this disease will help to prevent the fatal consequences. To diagnose the disease requires some laboratory tests in which the results of the test will be calculated and summed up by a doctor or medical practitioner. The development of science and technology, especially in the field of computers will help the doctor’s works in analyzing the results of laboratory test easier and faster. By some data as training data and implementing Fuzzy Decision Tree classification algorithm, it is expected to obtain high accuracy results that can be used as a reference for predicting chronic renal failure and avoid the occurrence of fatal consequences. The test was conducted by using some predetermined threshold and obtained the most optimal accuracy 98.28% with which indicated a fairly high level of accuracy. Thus the Fuzzy Decision Tree algorithm can be said to be able to predict the disease of chronic renal failure by the accuracy 98.28%.

Reconfigurable Hardware for Machine Learning Applications

This paper proposes universal coarse-grained reconfigurable computing architecture for hardware implementation of decision trees (DTs), artificial neural networks (ANNs), and support vector machines (SVMs), suitable for both field programmable gate arrays (FPGA) and application specific integrated circuits (ASICs) implementation. Using this universal architecture, two versions of DTs (functional DT and axis-parallel DT), two versions of SVMs (with polynomial and radial kernel) and two versions of ANNs (multi layer perceptron ANN and radial basis ANN) machine learning classifiers, have been implemented in FPGA. Experimental results, based on 18 benchmark datasets of standard UCI machine learning repository database, show that FPGA implementation provides significant improvement (1–2 orders of magnitude) in the average instance classification time, in comparison with software implementations based on R project.

FPGA Implementation of Decision Trees and Tree Ensembles for Character Recognition in Vivado Hls

Abstract Decision trees and decision tree ensembles are popular machine learning methods, used for classification and regression. In this paper, an FPGA implementation of decision trees and tree ensembles for letter and digit recognition in Vivado High-Level Synthesis is presented. Two publicly available datasets were used at both training and testing stages. Different optimizations for tree code and tree node layout in memory are considered. Classification accuracy, throughput and resource usage for different training algorithms, tree depths and ensemble sizes are discussed. The correctness of the module’s operation was verified using C/RTL cosimulation and on a Zynq-7000 SoC device, using Xillybus IP core for data transfer between the processing system and the programmable logic.

An Intelligent Decision Support System for Cropland Suitability Evaluation

This paper describes a Service Oriented Architecture SOA based on Web services technology designed to assist producers in precision farming in the implementation of decision tree based Cropland Suitability Evaluation CSE decisions. The proposed SOA delivers a recommendation service to carry out CSE. The recommendation service is supported by three components that perform the knowledge discovery process by data extraction Data Provider, refinement of relevant dataset formats Data Refinement Assistant and data-driven estimation of significances-by-attributes Attribute Evaluator. We also suggest several data extraction functions in knowledge discovery process, the data refinement method transforming naive candidate datasets into compact and unified ones and the data-driven Attribute Significance Quantification ASQ algorithm for quantifying the influence of attributes on the decision tree.

Meteorological parameters and pollution caused by Sulfur dioxide and their influence on construction materials and heritage

In work is presented research of presence Sulfur dioxide (SO2) on sample place where is intensive traffic and population density is high. Air quality monitoring was done with an automatic station. For the interpretation of the results are used monthly and annual patterns. On the basis of the detected pollutants during the air monitoring show the state of environmental conditions in terms of air pollution with Sulfur dioxide and influence of them on aero-pollution, structural materials and building heritage. Also is given evaluation of influence polluted air on building heritage. For statistical data processing and modeling of pollution along with meteorological parameters was used decision tree implementation of the analytical and statistical tool SPSS 17.

Classification of haemoglobin typing chromatograms by neural networks and decision trees for thalassaemia screening

This article presents an application of a neural network and decision trees in thalassaemia screening. The aim is to classify thirteen classes of thalassaemia abnormality and one control class by inspecting the distribution of multiple types of haemoglobin in blood specimens, which are identified via high performance liquid chromatography (HPLC). C4.5 and random forests are the chosen architecture for decision tree implementation. For comparison, multilayer perceptrons are explored in classification via a neural network. The stratified 10-fold cross-validation results indicate that the best classification performance with overall accuracy of 97.2% (sensitivity = 97.2% and specificity = 99.8%) is achieved when C4.5 is used in conjunction with samples which have been pre-processed with input attribute discretisation and redundant attribute removal. Subsequently, C4.5 is applied to an additional sample set in a clinical trial which results in overall accuracy of 93.1% (sensitivity = 93.1% and specificity = 99.5%). These results suggest that a combination of C4.5 with haemoglobin typing analysis via HPLC may give rise to a guideline for further investigation of thalassaemia classification.

Intellectual property core implementation of decision trees

Several soft intellectual property (IP) core implementations of decision trees (axis-parallel, oblique and nonlinear) based on the concept of universal node (UN) and sequence of UNs are presented. Proposed IP cores are suitable for implementation in both field programmable gate arrays and application specific integrated circuits. Developed IP cores can be easily customised in order to fit a wide variety of application requirements, fulfilling their role as general purpose building blocks for SoC designs. Experimental results obtained on 23 data sets of standard UCI machine learning repository database suggest that the proposed architecture based on the sequence of UNs requires on average 56% less hardware resources compared with previously proposed architectures, having the same throughput.