Comparative approach of decision tree and CWQI analysis for classification of groundwater with a special reference to fluoride ion in drought-prone Boudh district of Odisha, India

2021 ◽  
Vol 7 (6) ◽  
Author(s):  
Subhasmita Barad ◽  
ParathaSarathi Mishra ◽  
Pramod Chandra Sahu ◽  
Tanmay Sarkar ◽  
Mohamad Faiz Mohd Amin ◽  
...  
Keyword(s):  
Decision Tree ◽  
Special Reference ◽  
Comparative Approach ◽  
Fluoride Ion

scholarly journals Decision Tree C 4.5 Algorithm for Classification of Poor Family Scholarship Recipients

2021 ◽  
Vol 1125 (1) ◽  
pp. 012048
Author(s):  
Y Kustiyahningsih ◽  
B K Khotimah ◽  
D R Anamisa ◽  
M Yusuf ◽  
T Rahayu ◽  
...  
Keyword(s):  
Decision Tree ◽  
Poor Family ◽  
Scholarship Recipients

scholarly journals 174 A comparison of machine learning algorithms in the classification of beef steers finished in feedlot

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 126-127
Author(s):  
Lucas S Lopes ◽  
Christine F Baes ◽  
Dan Tulpan ◽  
Luis Artur Loyola Chardulo ◽  
Otavio Machado Neto ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Final Decision ◽  
Relevant Parameter ◽  
Good Prediction ◽  
Quality Traits ◽  
C4.5 Decision Tree

Abstract The aim of this project is to compare some of the state-of-the-art machine learning algorithms on the classification of steers finished in feedlots based on performance, carcass and meat quality traits. The precise classification of animals allows for fast, real-time decision making in animal food industry, such as culling or retention of herd animals. Beef production presents high variability in its numerous carcass and beef quality traits. Machine learning algorithms and software provide an opportunity to evaluate the interactions between traits to better classify animals. Four different treatment levels of wet distiller’s grain were applied to 97 Angus-Nellore animals and used as features for the classification problem. The C4.5 decision tree, Naïve Bayes (NB), Random Forest (RF) and Multilayer Perceptron (MLP) Artificial Neural Network algorithms were used to predict and classify the animals based on recorded traits measurements, which include initial and final weights, sheer force and meat color. The top performing classifier was the C4.5 decision tree algorithm with a classification accuracy of 96.90%, while the RF, the MLP and NB classifiers had accuracies of 55.67%, 39.17% and 29.89% respectively. We observed that the final decision tree model constructed with C4.5 selected only the dry matter intake (DMI) feature as a differentiator. When DMI was removed, no other feature or combination of features was sufficiently strong to provide good prediction accuracies for any of the classifiers. We plan to investigate in a follow-up study on a significantly larger sample size, the reasons behind DMI being a more relevant parameter than the other measurements.

scholarly journals Classification of diabetes disease using decision tree algorithm (C4.5)

2021 ◽  
Vol 1869 (1) ◽  
pp. 012082
Author(s):  
B A C Permana ◽  
R Ahmad ◽  
H Bahtiar ◽  
A Sudianto ◽  
I Gunawan
Keyword(s):  
Decision Tree ◽  
Decision Tree Algorithm ◽  
Tree Algorithm

Improved differentiation classification of variable precision artificial intelligence higher education management

2021 ◽  
pp. 1-10
Author(s):  
Chao Dong ◽  
Yan Guo
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Data Mining ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Attribute Selection ◽  
Higher Education Management ◽  
Education Management ◽  
Decision Tree Classification

The wide application of artificial intelligence technology in various fields has accelerated the pace of people exploring the hidden information behind large amounts of data. People hope to use data mining methods to conduct effective research on higher education management, and decision tree classification algorithm as a data analysis method in data mining technology, high-precision classification accuracy, intuitive decision results, and high generalization ability make it become a more ideal method of higher education management. Aiming at the sensitivity of data processing and decision tree classification to noisy data, this paper proposes corresponding improvements, and proposes a variable precision rough set attribute selection standard based on scale function, which considers both the weighted approximation accuracy and attribute value of the attribute. The number improves the anti-interference ability of noise data, reduces the bias in attribute selection, and improves the classification accuracy. At the same time, the suppression factor threshold, support and confidence are introduced in the tree pre-pruning process, which simplifies the tree structure. The comparative experiments on standard data sets show that the improved algorithm proposed in this paper is better than other decision tree algorithms and can effectively realize the differentiated classification of higher education management.

A Decision Tree Can Increase Accuracy When Assessing Curve Types According to Lenke Classification of Adolescent Idiopathic Scoliosis

Spine ◽  
2010 ◽  
Vol 35 (10) ◽  
pp. 1054-1059 ◽  
Author(s):  
Philippe Phan ◽  
Neila Mezghani ◽  
Marie-Lyne Nault ◽  
Carl-Éric Aubin ◽  
Stefan Parent ◽  
...  
Keyword(s):  
Adolescent Idiopathic Scoliosis ◽  
Decision Tree ◽  
Idiopathic Scoliosis ◽  
Lenke Classification

Инедиты калмыцкого фольклора из фонда И. И. Попова: тематическая классификация образцов устной несказочной прозы

2020 ◽  
Vol 15 (3) ◽  
pp. 233-250
Author(s):  
Danara V. Ubushieva ◽  
Keyword(s):  
Russian Translation ◽  
Comparative Approach

The purpose of the article is to classify samples of oral non-fabulous prose recorded by I. I. Popov, based on a generally accepted comparative approach. The material for the study was the manuscript notebook “Old legends of the Don Kalmyks in the original Kalmyk texts and Russian translation” from the collection of Don Kalmyks folklore collector I. I. Popov. Results. The thematic classification of samples of oral non-fabulous prose of Don Kalmyks has the following structure: six myths (five etiological myths about the origin and features of animals, birds, insects, plants, nature phenomena and one calendar myth), seven stories (two of religious content and four — historical), two legends about the origin of customs, rites, rituals and one sample could not be classified as it is incomplete. Hence, out of seventeen samples — nine (No. 3, 4, 5a, 6, 7, 8, 11, 12, 13) do not have any variations and some of them are included into the collections “Seven Stars” and “Myths, Legends and Traditions of Kalmyks”. There are variations or versions for seven samples (No. 1, 2, 5b, 9, 10, 14, 15); however, it should be noted that some samples have not been published in I. I. Popov’s recordings, and are published in the recordings of other collectors. One sample (No. 16) is not classified, thus, there are certain difficulties in the analysis of its variants or versions.

scholarly journals Modified Decision Tree Technique for Ransomware Detection at Runtime through API Calls

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Faizan Ullah ◽  
Qaisar Javaid ◽  
Abdu Salam ◽  
Masood Ahmad ◽  
Nadeem Sarwar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Feature Vector ◽  
Machine Learning Algorithms ◽  
The Novel ◽  
Proposed Model ◽  
Testing Accuracy ◽  
Financial Losses

Ransomware (RW) is a distinctive variety of malware that encrypts the files or locks the user’s system by keeping and taking their files hostage, which leads to huge financial losses to users. In this article, we propose a new model that extracts the novel features from the RW dataset and performs classification of the RW and benign files. The proposed model can detect a large number of RW from various families at runtime and scan the network, registry activities, and file system throughout the execution. API-call series was reutilized to represent the behavior-based features of RW. The technique extracts fourteen-feature vector at runtime and analyzes it by applying online machine learning algorithms to predict the RW. To validate the effectiveness and scalability, we test 78550 recent malign and benign RW and compare with the random forest and AdaBoost, and the testing accuracy is extended at 99.56%.

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