Decision Tree-Based Classification Model to Predict Student Employability

2021 ◽  
pp. 327-333
Author(s):  
Chandra Patro ◽  
Indrajit Pan
Keyword(s):  
Decision Tree ◽  
Classification Model

scholarly journals Design of Decision Tree Structure with Improved BPNN Nodes for High-Accuracy Locomotion Mode Recognition Using a Single IMU

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 526
Author(s):  
Yang Han ◽  
Chunbao Liu ◽  
Lingyun Yan ◽  
Lei Ren
Keyword(s):  
Decision Tree ◽  
Time Window ◽  
Threshold Value ◽  
Recognition System ◽  
Tree Structure ◽  
Classification Model ◽  
Measurement Unit ◽  
Local Optima ◽  
Mode Recognition ◽  
Locomotion Mode

Smart wearable robotic system, such as exoskeleton assist device and powered lower limb prostheses can rapidly and accurately realize man–machine interaction through locomotion mode recognition system. However, previous locomotion mode recognition studies usually adopted more sensors for higher accuracy and effective intelligent algorithms to recognize multiple locomotion modes simultaneously. To reduce the burden of sensors on users and recognize more locomotion modes, we design a novel decision tree structure (DTS) based on using an improved backpropagation neural network (IBPNN) as judgment nodes named IBPNN-DTS, after analyzing the experimental locomotion mode data using the original values with a 200-ms time window for a single inertial measurement unit to hierarchically identify nine common locomotion modes (level walking at three kinds of speeds, ramp ascent/descent, stair ascent/descent, Sit, and Stand). In addition, we reduce the number of parameters in the IBPNN for structure optimization and adopted the artificial bee colony (ABC) algorithm to perform global search for initial weight and threshold value to eliminate system uncertainty because randomly generated initial values tend to result in a failure to converge or falling into local optima. Experimental results demonstrate that recognition accuracy of the IBPNN-DTS with ABC optimization (ABC-IBPNN-DTS) was up to 96.71% (97.29% for the IBPNN-DTS). Compared to IBPNN-DTS without optimization, the number of parameters in ABC-IBPNN-DTS shrank by 66% with only a 0.58% reduction in accuracy while the classification model kept high robustness.

A DECISION TREE-BASED CLASSIFICATION APPROACH TO RULE EXTRACTION FOR SECURITY ANALYSIS

2006 ◽  
Vol 05 (01) ◽  
pp. 227-240 ◽  
Author(s):  
N. REN ◽  
M. ZARGHAM ◽  
S. RAHIMI
Keyword(s):  
Decision Tree ◽  
High Performance ◽  
Security Analysis ◽  
Predictive Performance ◽  
Classification Model ◽  
Stock Selection ◽  
Selection Rules ◽  
Stock Portfolios ◽  
Decision Tree Classification ◽  
C4.5 Decision Tree

Stock selection rules are extensively utilized as the guideline to construct high performance stock portfolios. However, the predictive performance of the rules developed by some economic experts in the past has decreased dramatically for the current stock market. In this paper, C4.5 decision tree classification method was adopted to construct a model for stock prediction based on the fundamental stock data, from which a set of stock selection rules was derived. The experimental results showed that the generated rules have exceptional predictive performance. Moreover, it also demonstrated that the C4.5 decision tree classification model can work efficiently on the high noise stock data domain.

scholarly journals DECISION TREE C4.5 ALGORITHM FOR TUITION AID GRANT PROGRAM CLASSIFICATION (CASE STUDY: DEPARTMENT OF INFORMATION SYSTEM, UNIVERSITAS TEKNOKRAT INDONESIA)

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Heni Sulistiani ◽  
Ahmad Ari Aldino
Keyword(s):  
Decision Tree ◽  
Classification Model ◽  
Grant Program ◽  
C4.5 Algorithm ◽  
Long Time ◽  
The Right ◽  
Short Time ◽  
Tuition Fee

In pandemic era, almost everyone struggles for their life. College students are such example. They have difficulty in paying tuition fee to continue their study. Based on this problematic situation, Universitas Teknokrat Indonesia grants the students who have good academic performance with tuition fee aid program. Many variables used for determining the grant made it hard to make a decision in a short time or even takes very long time. To make it easier for management to decide who is the right student to get grant, it needs classification model. The purpose of this study is the classification of grant recipients by using decision tree C4.5 algorithm. That can determine whether a potential student can be accepted as an awardee or not. Then, the results of the classification are validated with ten-fold cross validation with an accuracy, precision and recall with the score of 87 % for all part. It means the model perform quite well to be implemented into system.

Multi-Class Taxonomy of Well Integrity Anomalies Applying Inductive Learning Algorithms: Analytical Approach for Artificial-Lift Wells

2021 ◽  
Author(s):  
Mostafa Sa'eed Yakoot ◽  
Adel Mohamed Salem Ragab ◽  
Omar Mahmoud
Keyword(s):  
Decision Tree ◽  
Confusion Matrix ◽  
Learning Algorithms ◽  
Oil And Gas Industry ◽  
Classification Model ◽  
Gradient Boosting ◽  
Support Vector ◽  
Risk Category ◽  
Well Integrity ◽  
Extreme Gradient Boosting

Abstract Well integrity has become a crucial field with increased focus and being published intensively in industry researches. It is important to maintain the integrity of the individual well to ensure that wells operate as expected for their designated life (or higher) with all risks kept as low as reasonably practicable, or as specified. Machine learning (ML) and artificial intelligence (AI) models are used intensively in oil and gas industry nowadays. ML concept is based on powerful algorithms and robust database. Developing an efficient classification model for well integrity (WI) anomalies is now feasible because of having enormous number of well failures and well barrier integrity tests, and analyses in the database. Circa 9000 dataset points were collected from WI tests performed for 800 wells in Gulf of Suez, Egypt for almost 10 years. Moreover, those data have been quality-controlled and quality-assured by experienced engineers. The data contain different forms of WI failures. The contributing parameter set includes a total of 23 barrier elements. Data were structured and fed into 11 different ML algorithms to build an automated systematic tool for calculating imposed risk category of any well. Comparison analysis for the deployed models was performed to infer the best predictive model that can be relied on. 11 models include both supervised and ensemble learning algorithms such as random forest, support vector machine (SVM), decision tree and scalable boosting techniques. Out of 11 models, the results showed that extreme gradient boosting (XGB), categorical boosting (CatBoost), and decision tree are the most reliable algorithms. Moreover, novel evaluation metrics for confusion matrix of each model have been introduced to overcome the problem of existing metrics which don't consider domain knowledge during model evaluation. The innovated model will help to utilize company resources efficiently and dedicate personnel efforts to wells with the high-risk. As a result, progressive improvements on business, safety, environment, and performance of the business. This paper would be a milestone in the design and creation of the Well Integrity Database Management Program through the combination of integrity and ML.

Quality of Life Modeling at the Regional Level

2012 ◽  
pp. 163-186
Author(s):  
Jirí Krupka ◽  
Miloslava Kašparová ◽  
Pavel Jirava ◽  
Jan Mandys
Keyword(s):  
Quality Of Life ◽  
Czech Republic ◽  
Decision Tree ◽  
Decision Rules ◽  
Real Data ◽  
Classification Model ◽  
Data Sets ◽  
The Czech Republic ◽  
First Case

The chapter presents the problem of quality of life modeling in the Czech Republic based on classification methods. It concerns a comparison of methodological approaches; in the first case the approach of the Institute of Sociology of the Academy of Sciences of the Czech Republic was used, the second case is concerning a project of the civic association Team Initiative for Local Sustainable Development. On the basis of real data sets from the institute and team initiative the authors synthesized and analyzed quality of life classification models. They used decision tree classification algorithms for generating transparent decision rules and compare the classification results of decision tree. The classifier models on the basis of C5.0, CHAID, C&RT and C5.0 boosting algorithms were proposed and analyzed. The designed classification model was created in Clementine.

scholarly journals Classifying the Level of Energy-Environmental Efficiency Rating of Brazilian Ethanol

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2067
Author(s):  
Nilsa Duarte da Silva Lima ◽  
Irenilza de Alencar Nääs ◽  
João Gilberto Mendes dos Reis ◽  
Raquel Baracat Tosi Rodrigues da Silva
Keyword(s):  
Decision Tree ◽  
High Efficiency ◽  
Rating Scale ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Environmental Efficiency ◽  
Classification Model ◽  
Bayes Algorithm ◽  
J48 Decision Tree

The present study aimed to assess and classify energy-environmental efficiency levels to reduce greenhouse gas emissions in the production, commercialization, and use of biofuels certified by the Brazilian National Biofuel Policy (RenovaBio). The parameters of the level of energy-environmental efficiency were standardized and categorized according to the Energy-Environmental Efficiency Rating (E-EER). The rating scale varied between lower efficiency (D) and high efficiency + (highest efficiency A+). The classification method with the J48 decision tree and naive Bayes algorithms was used to predict the models. The classification of the E-EER scores using a decision tree using the J48 algorithm and Bayesian classifiers using the naive Bayes algorithm produced decision tree models efficient at estimating the efficiency level of Brazilian ethanol producers and importers certified by the RenovaBio. The rules generated by the models can assess the level classes (efficiency scores) according to the scale discretized into high efficiency (Classification A), average efficiency (Classification B), and standard efficiency (Classification C). These results might generate an ethanol energy-environmental efficiency label for the end consumers and resellers of the product, to assist in making a purchase decision concerning its performance. The best classification model was naive Bayes, compared to the J48 decision tree. The classification of the Energy Efficiency Note levels using the naive Bayes algorithm produced a model capable of estimating the efficiency level of Brazilian ethanol to create labels.

Glaucoma Classification Model Based on GDx VCC Measured Parameters by Decision Tree

2009 ◽  
Vol 34 (6) ◽  
pp. 1141-1147 ◽  
Author(s):  
Mei-Ling Huang ◽  
Hsin-Yi Chen
Keyword(s):  
Decision Tree ◽  
Classification Model ◽  
Model Based ◽  
Gdx Vcc
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