scholarly journals Building student’s performance decision tree classifier using boosting algorithm

2019 ◽  
Vol 14 (3) ◽  
pp. 1298 ◽  
Author(s):  
Farid Jauhari ◽  
Ahmad Afif Supianto
Keyword(s):  
Student Performance ◽  
Binary Classification ◽  
Training Data ◽  
Special Treatment ◽  
Decision Tree Classifier ◽  
Baseline Method ◽  
Tree Classifier ◽  
Boosting Algorithms ◽  
Student’S Performance ◽  
Boosting Algorithm

<span lang="EN-US">Student’s performance is the most important value of the educational institutes for their competitiveness. In order to improve the value, they need to predict student’s performance, so they can give special treatment to the student that predicted as low performer. In this paper, we propose 3 boosting algorithms (C5.0, adaBoost.M1, and adaBoost.SAMME) to build the classifier for predicting student’s performance. This research used <sup>1</sup>UCI student performance datasets. There are 3 scenarios of evaluation, the first scenario was employ 10-fold cross-validation to compare performance of boosting algorithms. The result of first scenario showed that adaBoost.SAMME and adaBoost.M1 outperform baseline method in binary classification. The second scenario was used to evaluate boosting algorithms under different number of training data. On the second scenario, adaBoost.M1 was outperformed another boosting algorithms and baseline method on the binary classification. As third scenario, we build models from one subject dataset and test using onother subject dataset. The third scenario results indicate that it can build prediction model using one subject to predict another subject.</span>

scholarly journals Student Academic Performance Prediction using Supervised Learning Techniques

2019 ◽  
Vol 14 (14) ◽  
pp. 92 ◽  
Author(s):  
Muhammad Imran ◽  
Shahzad Latif ◽  
Danish Mehmood ◽  
Muhammad Saqlain Shah
Keyword(s):  
Data Mining ◽  
Supervised Learning ◽  
Student Performance ◽  
Performance Prediction ◽  
Class Imbalance ◽  
Ensemble Methods ◽  
Fine Tuning ◽  
Classification Error ◽  
Decision Tree Classifier ◽  
Tree Classifier

Automatic Student performance prediction is a crucial job due to the large volume of data in educational databases. This job is being addressed by educational data mining (EDM). EDM develop methods for discovering data that is derived from educational environment. These methods are used for understanding student and their learning environment. The educational institutions are often curious that how many students will be pass/fail for necessary arrangements. In previous studies, it has been observed that many researchers have intension on the selection of appropriate algorithm for just classification and ignores the solutions of the problems which comes during data mining phases such as data high dimensionality ,class imbalance and classification error etc. Such types of problems reduced the accuracy of the model. Several well-known classification algorithms are applied in this domain but this paper proposed a student performance prediction model based on supervised learning decision tree classifier. In addition, an ensemble method is applied to improve the performance of the classifier. Ensemble methods approach is designed to solve classification, predictions problems. This study proves the importance of data preprocessing and algorithms fine-tuning tasks to resolve the data quality issues. The experimental dataset used in this work belongs to Alentejo region of Portugal which is obtained from UCI Machine Learning Repository. Three supervised learning algorithms (J48, NNge and MLP) are employed in this study for experimental purposes. The results showed that J48 achieved highest accuracy 95.78% among others.

scholarly journals Student Performance Predictions Using Knowledge Discovery Database and Data Mining, DPU Students Records as Sample

2021 ◽  
Vol 10 (3) ◽  
pp. 121-127
Author(s):  
Bareen Haval ◽  
Karwan Jameel Abdulrahman ◽  
Araz Rajab
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Student Performance ◽  
Educational Data Mining ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Data Mining Techniques ◽  
Academic History ◽  
Tree Classifier ◽  
Using Data

This article presents the results of connecting an educational data mining techniques to the academic performance of students. Three classification models (Decision Tree, Random Forest and Deep Learning) have been developed to analyze data sets and predict the performance of students. The projected submission of the three classificatory was calculated and matched. The academic history and data of the students from the Office of the Registrar were used to train the models. Our analysis aims to evaluate the results of students using various variables such as the student's grade. Data from (221) students with (9) different attributes were used. The results of this study are very important, provide a better understanding of student success assessments and stress the importance of data mining in education. The main purpose of this study is to show the student successful forecast using data mining techniques to improve academic programs. The results of this research indicate that the Decision Tree classifier overtakes two other classifiers by achieving a total prediction accuracy of 97%.

scholarly journals Prediction of Student Performance Using Decision Tree Classifier

2019 ◽  
Vol 131 ◽  
pp. 1-12
Author(s):  
V Ramakrishna Sajja ◽  
P Jhansi Lakshmi ◽  
DS Bhupal Naik ◽  
Hemantha Kumar Kalluri
Keyword(s):  
Decision Tree ◽  
Student Performance ◽  
Decision Tree Classifier ◽  
Tree Classifier

scholarly journals SISTEM DETEKSI DINI UNTUK MENINGKATKAN PERFORMANCE KELULUSAN MAHASISWA DENGAN ID3

2018 ◽  
Author(s):  
Slamet Kacung ◽  
Budi Santoso
Keyword(s):  
Higher Education ◽  
Early Detection ◽  
Decision Tree ◽  
Student Performance ◽  
Detection System ◽  
Academic Programs ◽  
Decision Tree Classifier ◽  
Study Program ◽  
Tree Classifier

The performance of academic programs higher education is measured by the number of graduates are produced by each study program as reflected in the standard III accreditation form in point 3.1.1 and 3.1.4. The study program is required to have a good performance is marked by the increasing number of graduates in proportion to the number of students received so that ratio lecturers with students can be maintained. The more students who are accepted in college if they are not comparable with the number of graduates in each year will have an impact on the quality of learning. The result of this graduation becomes the evaluation material of the study program which will be the input of the study program and the Academic Advisors (DPAM) in order to provide treatment to the problem students so that they can improve the performance of the graduates. DPAM has a very important role in the progress of the learning process of students Guide, but with the amount of guidance that is increasingly causing students to be misdirected and in the end the student performance becomes bad, for that need an early detection system to improve the performance of graduates based on the results of the recommendation from the decision tree classifier. this method can generate a decision tree and give recommendations to students problems with accuracy.

scholarly journals Expiry Prediction and Reducing Food Wastage using IoT and ML

2021 ◽  
Vol 12 (3) ◽  
pp. 155-162
Author(s):  
Kartik Nair ◽  
Bhavya Sekhani ◽  
Krina Shah ◽  
Sunil Karamchandani
Keyword(s):  
Binary Classification ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Decision Tree Classifier ◽  
Expiry Date ◽  
Oxide Semiconductors ◽  
Tree Classifier ◽  
Sensor Temperature ◽  
Food Wastage

This paper details development of a low-cost, small-size, and portable electronic nose (E-nose) for the prediction of the expiry date of food products. The Sensor array is composed of commercially available metal oxide semiconductors sensors like MQ2 sensor, temperature sensor, and humidity sensor, which were interfaced with the help of ESP8266 and Arduino Uno for data acquisition, storage, and analysis of the dataset consisting of the odor from the fruit at different ripening stages. The developed system is used to analyze gas sensor values from various fruits like bananas and tomatoes. Responding signals of the e-nose were extracted and analyzed. Based on the obtained data we applied a few machine learning algorithms to predict if a banana is stale or not. Logistic regression, Decision Tree Classifier, Support Vector Classifier (SVC) &amp; K-Nearest Neighbours (KNN) classifiers were the binary classification algorithms used to determine whether the fruit became stale or not. We achieved an accuracy of 97.05%. These results prove that e-nose has the potential of assessing fruits and vegetable freshness and predict their expiry date, thus reducing food wastage.

A MULTICLASS EXTENSION TO THE BROWNBOOST ALGORITHM

2004 ◽  
Vol 18 (05) ◽  
pp. 905-931 ◽  
Author(s):  
ROSS A. MCDONALD ◽  
DAVID J. HAND ◽  
IDRIS A. ECKLEY
Keyword(s):  
Binary Classification ◽  
Simulated Data ◽  
Training Data ◽  
Data Sets ◽  
Base Line ◽  
Classification Problems ◽  
Undue Influence ◽  
Basic Algorithm ◽  
Class Noise ◽  
Boosting Algorithm

Brownboost is an adaptive, continuous time boosting algorithm based on the Boost-by-Majority (BBM) algorithm. Though it has been little studied at the time of writing, it is believed that it should prove especially robust with respect to noisy data sets. This would make it a very useful boosting algorithm for real-world applications. More familiar algorithms such as Adaboost, or its successor Logitboost, are known to be especially susceptible to overfitting the training data examples. This can lead to a poor generalization error in the presence of class noise, since weak hypotheses induced at later iterations to fit the noisy examples will tend to be given undue influence in the final combined hypothesis. Brownboost allows us to specify an expected base-line error rate in advance, corresponding to our prior beliefs about the proportion of noise in the training data, and thus avoid overfitting. The original derivation of Brownboost is restricted to binary classification problems. In this paper we propose a natural multiclass extension to the basic algorithm, incorporating error-correcting output codes and a multiclass gain measure. We test two-class and multiclass versions of the algorithm on a number of real and simulated data sets with artificial class noise, and show that Brownboost consistently outperforms Adaboost in these situations.

K-means Clustering-based Radio Neutron Star Pulsar Emission Mechanism

2020 ◽  
Vol 13 ◽  
Author(s):  
Shubham Shrimali ◽  
Amritanshu Pandey ◽  
Chiranji Lal Chowdhary
Keyword(s):  
Neural Networks ◽  
Neutron Star ◽  
Decision Tree ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Emission Mechanism ◽  
Decision Tree Classifier ◽  
Emission Data ◽  
Pulsar Emission ◽  
Tree Classifier

: The aim of this paper is to work on K-means clustering-based radio neutron star pulsar emission mechanism. Background: The pulsars are a rare type of neutron star that produces radio rays. Such type of rays are detectable on earth and it attracts scientists because of its concern with space-time, interstellar medium, and states of matter. During the rotation of pulsar rays, it emits the rays in the whole sky and after crossing the threshold value, the pattern of radio emission broadband detected. As rotation speed of pulsar increases then accordingly the types of the pattern produced periodically. Every pulsar emits different patterns which are a little bit different from each other which is fully depends on its rotation. The detected signals are known as a candidate. Its length of observation can determine it and it is average of all rotation of pulsar. Objective: The main objectives of this radio neutron star pulsar emission mechanism are: (a) Decision Tree Classifier (2) K-means Clustering (3) Neural Networks. Method: The Pulsar Emission Data was broken down into two sets of data: Training Data and Testing Data. The Training Data used to train the Decision Tree The algorithm, K-means clustering, and Neural Networks to allow it to identify, which attributes (Training Labels), are useful for identification of Neutron Pulsar Emissions. Results: The analysis is using multiple machine learning algorithms; it concluded that using neural networks is the best possible method to detect pulsar emissions from neutron stars. The best result achieved is 98% using Neural Networks. Conclusion: There are so many benefits of pulsar rays in different technology. Earth can detect pulsar ray from low orbit. Earth can completely absorb X-ray in the atmosphere and from these; we can say that the wavelength is limited to those who do not have an atmosphere like space. The result we got according to that we can say that the algorithm we used successfully used for detecting the pulsar signals.

scholarly journals A Framework for AI-Assisted Detection of Patent Ductus Arteriosus from Neonatal Phonocardiogram

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 169
Author(s):  
Sergi Gómez-Quintana ◽  
Christoph E. Schwarz ◽  
Ihor Shelevytsky ◽  
Victoriya Shelevytska ◽  
Oksana Semenova ◽  
...  
Keyword(s):  
Patent Ductus Arteriosus ◽  
Heart Defects ◽  
Ductus Arteriosus ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Heart Sounds ◽  
Decision Tree Classifier ◽  
Support Tool ◽  
Tree Classifier ◽  
Patent Ductus

The current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical examination including (but not limited to) auscultation and pulse oximetry. While auscultation might be subjective with some heart abnormalities not always audible it increases the ability to detect heart defects. This work aims at developing an objective clinical decision support tool based on machine learning (ML) to facilitate differentiation of sounds with signatures of Patent Ductus Arteriosus (PDA)/CHDs, in clinical settings. The heart sounds are pre-processed and segmented, followed by feature extraction. The features are fed into a boosted decision tree classifier to estimate the probability of PDA or CHDs. Several mechanisms to combine information from different auscultation points, as well as consecutive sound cycles, are presented. The system is evaluated on a large clinical dataset of heart sounds from 265 term and late-preterm newborns recorded within the first six days of life. The developed system reaches an area under the curve (AUC) of 78% at detecting CHD and 77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of accuracy achieved by an experienced neonatologist when assessed on the same cohort.

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