scholarly journals C4.5 Algorithm To Predict The Impact Of The Earthquake

2017 ◽  
Author(s):  
Robbi Rahim ◽  
Efori Buulolo ◽  
Natalia Silalahi ◽  
Fadlina
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Tree Form ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
The Impact

One of the impacts of the quake was heavily damaged, the even tsunami killed at no less. One cause many deaths is because many can not predict the impact of earthquakes. Data earthquakes that occurred earlier can be used to predict the incidence of the quake will probably happen someday. One algorithm that can be used to predict is the algorithm C4.5. The results of the algorithm C4.5 decision tree form, decision trees characteristic or condition of the earthquake and the decision, where the decision is a fruit of the earthquake that occurred modeling

Research on Asset Recognition Algorithm of Information Security Product Based on Decision Tree Algorithm

2013 ◽  
Vol 397-400 ◽  
pp. 2296-2300 ◽  
Author(s):  
Fei Shuai ◽  
Jun Quan Li
Keyword(s):  
Computational Complexity ◽  
Information Security ◽  
Decision Tree ◽  
Recognition Algorithm ◽  
Space Complexity ◽  
Complex Relationship ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree

In current, there are complex relationship between the assets of information security product. According to this characteristic, we propose a new asset recognition algorithm (ART) on the improvement of the C4.5 decision tree algorithm, and analyze the computational complexity and space complexity of the proposed algorithm. Finally, we demonstrate that our algorithm is more precise than C4.5 algorithm in asset recognition by an application example whose result verifies the availability of our algorithm.Keywordsdecision tree, information security product, asset recognition, C4.5

The Analysis and Application of the C4.5 Algorithm in Decision Tree Technology

2012 ◽  
Vol 457-458 ◽  
pp. 754-757
Author(s):  
Hong Yan Zhao
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Teaching Quality ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
Result Analysis

The Decision Tree technology, which is the main technology of the Data Mining classification and forecast, is the classifying rule that infers the Decision Tree manifestation through group of out-of-orders, the non-rule examples. Based on the research background of The Decision Tree’s concept, the C4.5 Algorithm and the construction of The Decision Tree, the using of C4.5 Decision Tree Algorithm was applied to result analysis of students’ score for the purpose of improving the teaching quality.

The Water Conservancy Water Electricity Construction Engineering Professional Result Analysis of Application Base on C4.5 Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 703-707
Author(s):  
Hu Yong
Keyword(s):  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Construction Engineering ◽  
Improvement Algorithm ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
Selection Decision ◽  
Decision Tree Method ◽  
Result Analysis ◽  
Tree Method

Aimed at the student the result problem, give student the result data scoops out the model. The decision tree method is a very valid classification method, in the data that scoop out. According to student the result data characteristics, adopted the C4.5 decision tree algorithm. C4.5 algorithm is the improvement algorithm of the decision trees core algorithm ID3, it construct in brief, the speed compare quickly, easy realization. Selection decision belongs to sex, scoop out the result enunciation, that algorithm can be right to get student the result data classification, and some worthy conclusion, provide the decision the analysis.

Two-Stage Constructing Hyper-Plane for Each Test Node of Decision Tree

2010 ◽  
Vol 26-28 ◽  
pp. 776-779
Author(s):  
Wei She ◽  
Hong Li ◽  
Guo Qing Yu ◽  
Rui Deng
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Two Stage ◽  
Combination Methods ◽  
Hyper Plane ◽  
Two Stages ◽  
The Impact ◽  
Better Than

How to construct the “appropriate” split hyper-plane in test nodes is the key of building decision trees. Unlike a univariate decision tree, a multivariate (oblique) decision tree could find the hyper-plane that is not orthogonal to the features’ axes. In this paper, we re-explain the process of building test nodes in terms of geometry. Based on this, we propose a method of learning the hyper-plane with two stages. The tree (TSDT) induced in this way keeps the interpretability of univariate decision trees and the trait of multivariate decision trees which could find oblique hyper-plane. The tests of the impact of Combination methods tell us that TSDT based combination algorithm is much better than other tree based combination methods in accuracy.

scholarly journals METODE ALGORITMA DECISION TREE C4.5 UNTUK ANALISIS KELAYAKAN KREDIT NASABAH PADA BSM KCP KEMANG PRATAMA

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Dyah Wulandari ◽  
Nur Lutfiyana ◽  
Heny Sumarno
Keyword(s):  
Decision Tree ◽  
Profit Sharing ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Customer Data ◽  
A Value ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
Credit Worthiness

Abstract - Credit is the provision of money or equivalent claims, based on agreements or agreements on loans between banks and other parties which require the borrowing party to repay the debt after a certain period of time with the amount of interest, compensation or profit sharing. From the credit customer data available at BSM KCP Kemang Pratama still has Non Performing Financing (NPF) or Bad Credit.In analyzing a credit sometimes an analyst does an inaccurate analysis, so there are some customers who are less able to make credit payments, resulting in bad credit. So the researchers conducted an analysis using the C4.5 decision tree algorithm and Rapid Miner application for determining credit worthiness. From the analysis of credit customer data using the C4.5 decision tree algorithm method, the feasibility of credit recipient customers is very effective and produces a value of accuracy on Rapid Miner 5.3 of 80%, Precision of 100% and Recall of 0% so as to minimize the risk.Keywords— Credit, C4.5 Algorithm, Rapid Miner, Value AccuracyAbstrak - Kredit merupakan penyediaan uang atau tagihan yang dapat disamakan dengan hal itu, berdasarkan persetujuan atau kesepakatan pinjaman-pinjaman antara bank dengan pihak lain yang mewajibkan pihak peminjam untuk melunasi utangnya setelah jangka waktu tertentu dengan jumlah bunga, imbalan atau pembagian hasil keuntungan. Dari data nasabah kredit yang ada pada BSM KCP Kemang Pratama masih memiliki Non Performing Financing (NPF) atau Kredit Macet. Dalam menganalisa sebuah kredit terkadang seorang analis melakukan analisa tidak akurat, sehingga ada beberapa nasabah yang kurang mampu dalam melakukan pembayaran kredit, dan pada akhirnya mengakibatkan kredit macet. Peneliti melakukan analisis menggunakan algoritma decision tree C4.5 dan aplikasi Rapid Miner untuk penentuan kelayakan pemberian kredit. Dari analisis data nasabah kredit menggunakan metode Algoritma decision tree C4.5 menghasilkan kelayakan nasabah penerima kredit sangat efektif dan menghasilkan nilai akurasi pada Rapid Miner 5.3 sebesar 80%, Precision sebesar 100% dan Recall sebesar 0% sehingga dapat meminimalisir resiko yang terjadi.Kata kunci— Kredit, Algoritma C4.5, Rapid Miner, Nilai Akurasi

scholarly journals Penerapan Decision Tree Menggunakan Algoritma C4.5 Untuk Deteksi Demam Berdarah Pada RS. IMC Bintaro

2019 ◽  
Vol 5 (1) ◽  
pp. 75-86
Author(s):  
Farid Fadli ◽  
Belsana Butar Butar
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Data Processing ◽  
Death Rates ◽  
A Value ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree ◽  
Tree Data ◽  
Classification Category ◽  
Tree Method

Abstract: According to the WHO report in 2004, Indonesia is the largest country with the highest number of sufferers and death rates due to dengue fever. If it is not handled properly, the postponed treatment can be fatal. In this study, the authors used the kepuutsan tree method with C4.5 algorithm to process patient data to predict whether patients experienced bloody help regarding existing indications with the help of Rapidminer software. The results of data processing using Rapidminer were evaluated and validated with a confussion matrix and AUC curve, the results of data processing using the C4.5 algorithm had an accuracy of 72% and AUC had a value of 0.758 with a fair classification category. Keywords: Algorithm C4.5, Decision Tree, Data Mining

scholarly journals MENGIDENTIFIKASI HOAX PADA HASIL PENCARIAN BERITA ONLINE DENGAN TEKNIK WEB SCRAPING DAN ALGORITMA C4.5

2021 ◽  
Vol 6 (2) ◽  
pp. 130-137
Author(s):  
Diki Arisandi ◽  
Zul Indra ◽  
Kartini Kartini
Keyword(s):  
Decision Tree ◽  
Success Rate ◽  
Online News ◽  
Online Media ◽  
Web Scraping ◽  
The People ◽  
C4.5 Algorithm ◽  
News Credibility ◽  
The Impact ◽  
The Web

Online news is a journalistic product reports the facts or events that are produced and distributed via internet. However, not all of the information through online media is a real facts, also described as hoax. The large number of hoax news occurs, of course, deliver the impact on the people who look on the news, so it could cause misperceptions or inappropriate actions. We exploit a web scraping technique to extract the content from search search engines results. Furthermore, we employ the C4.5 algorithm for the classification process. There were three parameters as references: invitation to spread the news, credibility of the sources, and provoking title. The results of this work were a decision tree, that able to classify a news content as a hoax or legitimate. From the experiments which carried out, the accuracy of classification using the web scraping and C4.5 algorithm achieved 80% of success rate in determining the hoax.

scholarly journals Decision Tree based Classification and Dimensionality Reduction of Cervical Cancer

2020 ◽  
Vol 9 (4) ◽  
pp. 1531-1535
Keyword(s):  
Cervical Cancer ◽  
Decision Tree ◽  
Dimensionality Reduction ◽  
Reduction Technique ◽  
High Dimensional ◽  
Biological Processes ◽  
Decision Tree Algorithm ◽  
Dimensionality Reduction Technique ◽  
C4.5 Algorithm ◽  
C4.5 Decision Tree

The data revolution in medicines and biology have increased our fundamental understandings of biological processes and determining the factors causing any disease, but it has also posed a challenge towards their analysis. After breast cancer, most of the deaths among women are due to cervical cancer. According to IARC, alone in 2012 a noticeable number of cases estimated 7095 of cervical cancer were reported. 16.5% of the deaths were due to the cervical cancer with the total deaths of 28,711 among women. To analyze the high dimensional data with high accuracy and in less amount of time, their dimensionality needs to be reduced to remove irrelevant features. The classification is performed using the recent iteration in Quinlan’s C4.5 decision tree algorithm i.e. C5.0 algorithm and PCA as Dimensionality Reduction technique. Our proposed methodology has shown a significant improvement in the account of time taken by both algorithms. This shows that C5.0 algorithm is superior to C4.5 algorithm.

scholarly journals Using online decision trees to support students’ self-efficacy in the laboratory

2020 ◽  
Vol 44 (3) ◽  
pp. 430-435
Author(s):  
Sarah McLean ◽  
Ken N. Meadows ◽  
Austin Heffernan ◽  
Nicole Campbell
Keyword(s):  
Problem Solving ◽  
Decision Tree ◽  
Decision Trees ◽  
Self Efficacy ◽  
Extrinsic Motivation ◽  
Problem Solving Skills ◽  
Safe Place ◽  
Science Laboratories ◽  
Undergraduate Science ◽  
The Impact

Failed experiments are a common occurrence in research, yet many undergraduate science laboratories rely on established protocols to ensure students are able to obtain results. While it is logistically challenging to facilitate students’ conducting their own experiments in the laboratory, allowing students to “fail” in a safe environment could help with the development of problem-solving skills. To allow students a safe place to fail and encourage them to think through a laboratory protocol, online decision trees were created to lead students through protocols and give them timely feedback. The online decision trees present students with a scenario, then students execute a protocol by selecting options that will lead them down different paths and result in various realistic results from their experiments. They receive feedback and instructional tutorials throughout the simulation that are dependent on their choices. The significance of this new resource for student learning is that it allows students to practice their problem-solving skills and gain theoretical knowledge about the purpose of various experimental steps. The purpose of this research study was to evaluate whether online decision trees affected students’ self-efficacy, metacognition, and motivation for completing a wet laboratory. A mixed-methods approach was used; three surveys were administered throughout the academic term. For survey 1, students completed the decision tree and survey before the wet laboratory. For survey 2, students completed the survey before the wet laboratory but completed the decision tree after the wet laboratory. Students’ reported self-efficacy and intrinsic motivation were increased with the administration of the online decision trees before the wet laboratory, but their extrinsic motivation and metacognitive scores were unchanged. For survey 3, students provided written feedback about the impact of the online decision trees, and their responses highlighted the importance of the visual components of the approach.

Cross-Project Change Prediction Using Meta-Heuristic Techniques

2021 ◽  
pp. 279-299
Author(s):  
Ankita Bansal ◽  
Sourabh Jajoria
Keyword(s):  
Decision Tree ◽  
Decision Trees ◽  
Heuristic Algorithms ◽  
Software Systems ◽  
Evolutionary Learning ◽  
Tree Model ◽  
C4.5 Decision Tree ◽  
Change Prediction ◽  
Successful Change ◽  
Cross Project

Changes in software systems are inevitable. Identification of change-prone modules can help developers to focus efforts and resources on them. In this article, the authors conduct various intra-project and cross-project change predictions. The authors use distributional characteristics of dataset to generate rules which can be used for successful change prediction. The authors analyze the effectiveness of meta-heuristic decision trees in generating rules for successful cross-project change prediction. The employed meta-heuristic algorithms are hybrid decision tree genetic algorithms and oblique decision trees with evolutionary learning. The authors compare the performance of these meta-heuristic algorithms with C4.5 decision tree model. The authors observe that the accuracy of C4.5 decision tree is 73.33%, whereas the accuracy of the hybrid decision tree genetic algorithm and oblique decision tree are 75.00% and 75.56%, respectively. These values indicate that distributional characteristics are helpful in identifying suitable training set for cross-project change prediction.

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