scholarly journals Study of The ID3 and C4.5 Learning Algorithms

2020 ◽  
Vol 1 (2) ◽  
pp. 29-43
Author(s):  
Y. Fakir ◽  
M. Azalmad ◽  
R. Elaychi
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Decision Tree ◽  
Decision Trees ◽  
Information Gain ◽  
Learning Algorithms ◽  
Large Data ◽  
Classification Algorithms ◽  
Important Data

Data Mining is a process of exploring against large data to find patterns in decision-making. One of the techniques in decision-making is classification. Data classification is a form of data analysis used to extract models describing important data classes. There are many classification algorithms. Each classifier encompasses some algorithms in order to classify object into predefined classes. Decision Tree is one such important technique, which builds a tree structure by incrementally breaking down the datasets in smaller subsets. Decision Trees can be implemented by using popular algorithms such as ID3, C4.5 and CART etc. The present study considers ID3 and C4.5 algorithms to build a decision tree by using the “entropy” and “information gain” measures that are the basics components behind the construction of a classifier model

scholarly journals Mate-tree: un algoritmo para la tarea de clasificación basado en operadores algebraicos y primitivas SQL [Mate-tree: an Algorithm for classification task based on algebraic operators and SQL primitives]

2012 ◽  
Author(s):  
Ricardo Timarán Pereira
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Decision Trees ◽  
Information Gain ◽  
Classification Task ◽  
Classification Algorithms ◽  
Classification Rules ◽  
Decision Tree Classification ◽  
Expensive Process ◽  
Algebraic Operators

Resumen La clasificación basada en árboles de decisión es el modelo más utilizado y popular por su simplicidad y facilidad para su entendimiento. El cálculo del valor de la métrica que permite seleccionar, en cada nodo, el atributo que tenga una mayor potencia para clasificar sobre el conjunto de valores del atributo clase, es el proceso más costoso del algoritmo utilizado. Para calcular esta métrica, no se necesitan los datos, sino las estadísticas acerca del número de registros en los cuales se combinan los atributos condición con el atributo clase. Entre los algoritmos de clasificación por árboles de decisión se cuentan ID-3, C4.5, SPRINT y SLIQ. Sin embargo, ninguno de estos algoritmos se basan en operadores algebraicos relacionales y se implementa con primitivas SQL. En este artículo se presenta Mate-tree, un algoritmo para la tarea de minería de datos clasificación basado en los operadores algebraicos relacionales Mate, Entro, Gain y Describe Classifier, implementados en la cláusula SQL Select con las primitivas SQL Mate by, Entro(), Gain() y Describe Classification Rules, los cuales facilitan el cálculo de Ganancia de Información, la construcción del árbol de decisión y el acoplamiento fuerte de este algoritmo con un SGBD. Palabras ClavesÁrboles de Decisión, Minería de Datos, Operadores Algebraicos Relacionales, Primitivas SQL, Tarea de Clasificación.  Abstract Decision tree classification is the most used and popular model, because it is simple and easy to understand. The calculation of the value of the measure that allows selecting, in each node, the attribute with the highest power to classify on the set of values of the class attribute, is the most expensive process in the used algorithm. To compute this measure, the data are not needed, but the statistics about the number of records in which combine the test attributes with the class attribute. Among the classification algorithms by decision trees are ID-3, C4.5, SPRINT and SLIQ. However, none of these algorithms are based on relational algebraic operators and are implemented with SQL primitives. In this paper Mate-tree, an algorithm for the classification data mining task based on the relational algebraic operators Mate, Entro, Gain and Describe Classifier, is presented. They were implemented in the SQL Select clause with SQL primitives Mate by, Entro(), Gain() y Describe Classification Rules. They facilitate the calculation of the Information Gain, the construction of the decision tree and the tight coupled of this algorithm with a DBMS.KeywordsDecision Trees, Data Mining, Relational Algebraic Operators, SQL Primitives, Classification Task. 

Utilizing Fuzzy Decision Trees in Decision Making

2011 ◽  
pp. 2024-2030
Author(s):  
Malcolm J. Beynonm
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Decision Trees ◽  
Time Series Data ◽  
Series Data ◽  
Fuzzy Decision ◽  
Fuzzy Environment ◽  
Complex Decision Making ◽  
Fuzzy Decision Trees

The seminal work of Zadeh (1965), namely fuzzy set theory (FST), has developed into a methodology fundamental to analysis that incorporates vagueness and ambiguity. With respect to the area of data mining, it endeavours to find potentially meaningful patterns from data (Hu & Tzeng, 2003). This includes the construction of if-then decision rule systems, which attempt a level of inherent interpretability to the antecedents and consequents identified for object classification (See Breiman, 2001). Within a fuzzy environment this is extended to allow a linguistic facet to the possible interpretation, examples including mining time series data (Chiang, Chow, & Wang, 2000) and multi-objective optimisation (Ishibuchi & Yamamoto, 2004). One approach to if-then rule construction has been through the use of decision trees (Quinlan, 1986), where the path down a branch of a decision tree (through a series of nodes), is associated with a single if-then rule. A key characteristic of the traditional decision tree analysis is that the antecedents described in the nodes are crisp, where this restriction is mitigated when operating in a fuzzy environment (Crockett, Bandar, Mclean, & O’Shea, 2006). This chapter investigates the use of fuzzy decision trees as an effective tool for data mining. Pertinent to data mining and decision making, Mitra, Konwar and Pal (2002) succinctly describe a most important feature of decision trees, crisp and fuzzy, which is their capability to break down a complex decision-making process into a collection of simpler decisions and thereby, providing an easily interpretable solution.

scholarly journals C4.5 Classification Data Mining for Inventory Control

2018 ◽  
Vol 7 (2.3) ◽  
pp. 68 ◽  
Author(s):  
Robbi Rahim ◽  
Ilka Zufria ◽  
Nuning Kurniasih ◽  
Muhammad Yasin Simargolang ◽  
Abdurrozzaq Hasibuan ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Large Data ◽  
Production Costs ◽  
Traffic Data ◽  
Tree Form ◽  
Minimal Error ◽  
Decision Tree Method ◽  
Tree Method

Data Mining is a process of exploring against large data to find patterns in decision making. One of the techniques in decision-making is classification. Classification is a technique in data mining by applying decision tree method to form data, algorithm C4.5 is algorithm that can be used to classify data in tree form. The system has been built that shows the results of good performance and minimal error in view of the system that is able to distinguish the anomaly traffic with normal traffic. Data mining inventory system applications can facilitate the control of inventory in the company to reduce production costs. 

Creating Competitive Advantage by Using Data Mining Technique as an Innovative Method for Decision Making Process in Business

2011 ◽  
Vol 1 (3) ◽  
pp. 38-45 ◽  
Author(s):  
Mert Bal ◽  
Yasemin Bal ◽  
Ayse Demirhan
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Competitive Advantage ◽  
Large Data ◽  
Research Area ◽  
Decision Making Process ◽  
Success Factor ◽  
Organizational Capability ◽  
Data Mining Technique

Competitive advantage is at the heart of a firm’s performance in today’s challenging and rapidly changing environment. One of the central bases for achieving competitive advantage is the organizational capability to create new knowledge and transfer it across various levels of the organization. Traditional methods of data analysis, based mainly on human dealing directly with the data, simply do not scale to handle with large data sets. This explosive growth in data and databases has generated an urgent need for new techniques and tools that can intelligently and automatically transform the processed data into useful information and knowledge. Consequently, data mining has become a research area with increasing importance. Organizations of all sizes have started to develop and deploy data mining technologies to leverage data resources to enhance their decision making capabilities. Business information received from data analysis and data mining is a critical success factor for companies wishing to maximize competitive advantage. In this study, the importance of gaining knowledge for organizations in today’s competitive environment are discussed and data mining method in decision making process is analyzed as an innovative technique for organizations.

scholarly journals Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm

2021 ◽  
Vol 11 (15) ◽  
pp. 6728
Author(s):  
Muhammad Asfand Hafeez ◽  
Muhammad Rashid ◽  
Hassan Tariq ◽  
Zain Ul Abideen ◽  
Saud S. Alotaibi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tabu Search ◽  
Decision Tree ◽  
Decision Trees ◽  
Search Algorithm ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Tabu Search Algorithm

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.

scholarly journals Analisis Rekam Medis Pasien Diabetes Mellitus Melalui Implementasi Teknik Data Mining di RSUP Dr. Sardjito Yogyakarta

2018 ◽  
Vol 2 (2) ◽  
pp. 167
Author(s):  
Marko Ferdian Salim ◽  
Sugeng Sugeng
Keyword(s):  
Diabetes Mellitus ◽  
Data Mining ◽  
Decision Tree ◽  
Decision Trees ◽  
Cross Sectional ◽  
Diabetes Melitus

Latar Belakang: Diabetes mellitus adalah penyakit kronis yang mempengaruhi beban ekonomi dan sosial secara luas. Data pasien dicatat melalui sistem rekam medis pasien yang tersimpan dalam database sistem informasi rumah sakit, data yang tercatat belum dianalisis secara efektif untuk menghasilkan informasi yang berharga. Teknik data mining bisa digunakan untuk menghasilkan informasi yang berharga tersebut.Tujuan: Mengidentifikasi karakteristik pasien Diabetes mellitus, kecenderungan dan tipe Diabetes melitus melalui penerapan teknik data mining di RSUP Dr. Sardjito Yogyakarta.Metode: Penelitian ini merupakan penelitian deskriptif observasional dengan rancangan cross sectional. Teknik pengumpulan data dilakukan secara retrospektif melalui observasi dan studi dokumentasi rekam medis elektronik di RSUP Dr. Sardjito Yogyakarta. Data yang terkumpul kemudian dilakukan analisis dengan menggunakan aplikasi Weka.Hasil: Pasien Diabetes mellitus di RSUP Dr. Sardjito tahun 2011-2016 berjumlah 1.554 orang dengan tren yang cenderung menurun. Pasien paling banyak berusia 56 - 63 tahun (27,86%). Kejadian Diabetes mellitus didominasi oleh Diabetes mellitus tipe 2 dengan komplikasi tertinggi adalah hipertensi, nefropati, dan neuropati. Dengan menggunakan teknik data mining dengan algoritma decision tree J48 (akurasi 88.42%) untuk analisis rekam medis pasien telah menghasilkan beberapa rule.Kesimpulan: Teknik klasifikasi data mining (akurasi 88.42%) dan decision trees telah berhasil mengidentifikasi karakteristik pasien dan menemukan beberapa rules yang dapat digunakan pihak rumah sakit dalam pengambilan keputusan mengenai penyakit Diabetes mellitus.

Data Mining: A Bagged Decision Tree Classifier Algorithm For Ids Intrusion Detection System Based Attacks Classification

2021 ◽  
pp. 1826-1839
Author(s):  
Sandeep Adhikari, Dr. Sunita Chaudhary
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Decision Tree ◽  
Intrusion Detection System ◽  
Detection System ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Decision Tree Classifier ◽  
Tree Classifier

The exponential growth in the use of computers over networks, as well as the proliferation of applications that operate on different platforms, has drawn attention to network security. This paradigm takes advantage of security flaws in all operating systems that are both technically difficult and costly to fix. As a result, intrusion is used as a key to worldwide a computer resource's credibility, availability, and confidentiality. The Intrusion Detection System (IDS) is critical in detecting network anomalies and attacks. In this paper, the data mining principle is combined with IDS to efficiently and quickly identify important, secret data of interest to the user. The proposed algorithm addresses four issues: data classification, high levels of human interaction, lack of labeled data, and the effectiveness of distributed denial of service attacks. We're also working on a decision tree classifier that has a variety of parameters. The previous algorithm classified IDS up to 90% of the time and was not appropriate for large data sets. Our proposed algorithm was designed to accurately classify large data sets. Aside from that, we quantify a few more decision tree classifier parameters.

ANGEL Mining

2012 ◽  
pp. 94-115 ◽  
Author(s):  
Tyler Swanger ◽  
Kaitlyn Whitlock ◽  
Anthony Scime ◽  
Brendan P. Post
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Feature Selection ◽  
Decision Tree ◽  
Management System ◽  
Association Mining ◽  
Data Mining Techniques ◽  
Usage Patterns ◽  
Design Selection ◽  
Comprehensive College

This chapter data mines the usage patterns of the ANGEL Learning Management System (LMS) at a comprehensive college. The data includes counts of all the features ANGEL offers its users for the Fall and Spring semesters of the academic years beginning in 2007 and 2008. Data mining techniques are applied to evaluate which LMS features are used most commonly and most effectively by instructors and students. Classification produces a decision tree which predicts the courses that will use the ANGEL system based on course specific attributes. The dataset undergoes association mining to discover the usage of one feature’s effect on the usage of another set of features. Finally, clustering the data identifies messages and files as the features most commonly used. These results can be used by this institution, as well as similar institutions, for decision making concerning feature selection and overall usefulness of LMS design, selection and implementation.

Integrating OLAP/SOLAP in E-Business Domains

2011 ◽  
Vol 24 (3) ◽  
pp. 45-60
Author(s):  
Ben Ali ◽  
Samar Mouakket
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
United Arab Emirates ◽  
Decision Makers ◽  
Job Seekers ◽  
Job Market ◽  
Analysis Techniques ◽  
Analytical Processing ◽  
User Friendly

E-business domains have been considered killer domains for different data analysis techniques. Most researchers have examined data mining (DM) techniques to analyze the databases behind E-business websites. DM has shown interesting results, but this technique presents some restrictions concerning the content of the database and the level of expertise of the users interpreting the results. In this paper, the authors show that successful and more sophisticated results can be obtained using other analysis techniques, such as Online Analytical Processing (OLAP) and Spatial OLAP (SOLAP). Thus, the authors propose a framework that fuses or integrates OLAP with SOLAP techniques in an E-business domain to perform easier and more user-friendly data analysis (non-spatial and spatial) and improve decision making. In addition, the authors apply the framework to an E-business website related to online job seekers in the United Arab Emirates (UAE). The results can be used effectively by decision makers to make crucial decisions in the job market of the UAE.

A Hybrid Method for Prediction and Assessment Efficiency of Decision Making Units

2013 ◽  
Vol 5 (1) ◽  
pp. 66-83 ◽  
Author(s):  
Iman Rahimi ◽  
Reza Behmanesh ◽  
Rosnah Mohd. Yusuff
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Rules ◽  
Large Data ◽  
Poultry Meat ◽  
Small Data ◽  
Data Set ◽  
Data Mining Techniques ◽  
Decision Making Units

The objective of this article is an evaluation and assessment efficiency of the poultry meat farm as a case study with the new method. As it is clear poultry farm industry is one of the most important sub- sectors in comparison to other ones. The purpose of this study is the prediction and assessment efficiency of poultry farms as decision making units (DMUs). Although, several methods have been proposed for solving this problem, the authors strongly need a methodology to discriminate performance powerfully. Their methodology is comprised of data envelopment analysis and some data mining techniques same as artificial neural network (ANN), decision tree (DT), and cluster analysis (CA). As a case study, data for the analysis were collected from 22 poultry companies in Iran. Moreover, due to a small data set and because of the fact that the authors must use large data set for applying data mining techniques, they employed k-fold cross validation method to validate the authors’ model. After assessing efficiency for each DMU and clustering them, followed by applied model and after presenting decision rules, results in precise and accurate optimizing technique.

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