Comparative Analysis of Decision Tree Algorithms: ID3, C4.5 and Random Forest

2014 ◽  
pp. 549-562 ◽  
Author(s):  
Shiju Sathyadevan ◽  
Remya R. Nair
Keyword(s):  
Comparative Analysis ◽  
Random Forest ◽  
Decision Tree ◽  
Tree Algorithms

scholarly journals Performance Analysis of Predictive Models using Generic Datasets

2020 ◽  
Vol 9 (3) ◽  
pp. 3612-3617
Keyword(s):  
Neural Networks ◽  
Comparative Analysis ◽  
Performance Analysis ◽  
Decision Tree ◽  
Predictive Models ◽  
Misclassification Rate ◽  
Computational Time ◽  
Data Sets ◽  
Tree Algorithms

Today over 2.5 quintillion bytes of data is being created every single day where 753 crore people on this planet are creating 1.7mb of data each second. Most often than not, Researchers only scratch the surface when it comes to analyzing which algorithm will be best suited with their dataset and which one will give the highest efficiency. Sometimes, this analysis takes more computational time than the actual execution itself. Aim of this paper is to understand and solve this dilemma by applying different predictions models like Neural Networks, Regression and Decision Tree algorithms to different datasets where their performance was measured using ROC Index, Average Square Error and Misclassification Rate. A comparative analysis is done to show their best performance in different scopes and conditions. All data sets and results were compared and analyzed using SAS tool.

scholarly journals A Comparative Analysis of Machine/Deep Learning Models for Parking Space Availability Prediction

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 322 ◽  
Author(s):  
Faraz Malik Awan ◽  
Yasir Saleem ◽  
Roberto Minerva ◽  
Noel Crespi
Keyword(s):  
Deep Learning ◽  
Comparative Analysis ◽  
Random Forest ◽  
Decision Tree ◽  
Multilayer Perceptron ◽  
Large Data ◽  
Data Sets ◽  
Application Domain ◽  
Parking Space ◽  
Data Set

Machine/Deep Learning (ML/DL) techniques have been applied to large data sets in order to extract relevant information and for making predictions. The performance and the outcomes of different ML/DL algorithms may vary depending upon the data sets being used, as well as on the suitability of algorithms to the data and the application domain under consideration. Hence, determining which ML/DL algorithm is most suitable for a specific application domain and its related data sets would be a key advantage. To respond to this need, a comparative analysis of well-known ML/DL techniques, including Multilayer Perceptron, K-Nearest Neighbors, Decision Tree, Random Forest, and Voting Classifier (or the Ensemble Learning Approach) for the prediction of parking space availability has been conducted. This comparison utilized Santander’s parking data set, initiated while working on the H2020 WISE-IoT project. The data set was used in order to evaluate the considered algorithms and to determine the one offering the best prediction. The results of this analysis show that, regardless of the data set size, the less complex algorithms like Decision Tree, Random Forest, and KNN outperform complex algorithms such as Multilayer Perceptron, in terms of higher prediction accuracy, while providing comparable information for the prediction of parking space availability. In addition, in this paper, we are providing Top-K parking space recommendations on the basis of distance between current position of vehicles and free parking spots.

Employee's Attrition Prediction Using Machine Learning Approaches

2020 ◽  
pp. 121-128
Author(s):  
Krishna Kumar Mohbey
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Comparative Analysis ◽  
Random Forest ◽  
Decision Tree ◽  
Experimental Results ◽  
Learning Approaches ◽  
Employee Attrition ◽  
Or Organization ◽  
Customer Attrition

In any industry, attrition is a big problem, whether it is about employee attrition of an organization or customer attrition of an e-commerce site. If we can accurately predict which customer or employee will leave their current company or organization, then it will save much time, effort, and cost of the employer and help them to hire or acquire substitutes in advance, and it would not create a problem in the ongoing progress of an organization. In this chapter, a comparative analysis between various machine learning approaches such as Naïve Bayes, SVM, decision tree, random forest, and logistic regression is presented. The presented result will help us in identifying the behavior of employees who can be attired over the next time. Experimental results reveal that the logistic regression approach can reach up to 86% accuracy over other machine learning approaches.

Comparative Analysis of Decision Tree Algorithms for Data Warehouse Fragmentation

2021 ◽  
pp. 337-363
Author(s):  
Nidia Rodríguez-Mazahua ◽  
Lisbeth Rodríguez-Mazahua ◽  
Asdrúbal López-Chau ◽  
Giner Alor-Hernández ◽  
S. Gustavo Peláez-Camarena
Keyword(s):  
Comparative Analysis ◽  
Decision Tree ◽  
Data Warehouse ◽  
Tree Algorithms

scholarly journals Comparative Analysis of Intellectual Methods for Muscular Contraction Interpretation for Gesture Interface Implementation

2021 ◽  
Vol 2096 (1) ◽  
pp. 012190
Author(s):  
E V Bunyaeva ◽  
I V Kuznetsov ◽  
Y V Ponomarchuk ◽  
P S Timosh
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Logistic Regression ◽  
Comparative Analysis ◽  
Random Forest ◽  
Decision Tree ◽  
Single Channel ◽  
Muscular Contraction ◽  
Support Vector ◽  
Machine Learning Methods

Abstract The paper considers comparative analysis results of the machine learning methods used for the gesture recognition based on the surface single-channel electromyography (sEMG) data. The data were processed using multilayer perceptron, support vector machine, decision tree ensemble (Random Forest) and logistic regression for the chosen four gesture types. The conclusion was derived on the analysis efficiency of these methods using commonly recommended accuracy metrics.

scholarly journals Comparative Analysis of Decision Tree Algorithms for Data Warehouse Fragmentation*

2020 ◽  
Vol 7 (2-1) ◽  
pp. 31-43
Author(s):  
Nidia Rodríguez Mazahua ◽  
Lisbeth Rodríguez Mazahua ◽  
Asdrúbal López Chau ◽  
Giner Alor Hernández
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Decision Tree ◽  
Data Warehouse ◽  
Data Sets ◽  
Star Schema ◽  
Tree Algorithms ◽  
Horizontal Fragmentation ◽  
Roc Area ◽  
F Measure

One of the main problems faced by Data Warehouse designers is fragmentation.Several studies have proposed data mining-based horizontal fragmentation methods.However, not exists a horizontal fragmentation technique that uses a decision tree. This paper presents the analysis of different decision tree algorithms to select the best one to implement the fragmentation method. Such analysis was performed under version 3.9.4 of Weka, considering four evaluation metrics (Precision, ROC Area, Recall and F-measure) for different selected data sets using the Star Schema Benchmark. The results showed that the two best algorithms were J48 and Random Forest in most cases; nevertheless, J48 was selected because it is more efficient in building the model.

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