Analytical and CASE study on Limited Search, ID3, CHAID, C4.5, Improved C4.5 and OVA Decision Tree Algorithms to design Decision Support System

Kaur, Parneet; Singh, Sukhwinder; Garg, Sushil Kumar; Harmanpreet,; Patel, Ravindra; Singh, Bhupinder

doi:10.1063/1.3526277

Cited by 3 publications

(2 citation statements)

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“…According to the monitoring values of hydrochemical information of four types of water sources, in order to classify and predict these four types of water sources more intuitively and efficiently, the paper adopts the decision tree C4.5 algorithm model to automatically select the characteristics of hydrochemical information, and the information gain rate is used as the decision tree model to select the root node and internal node branch attribute [34]. The specific model principles: the data set is N, and the data set is divided into categories n. The number of sample data belonging to class i is c i , and the total number of sample data in N is |N|, then the probability of a sample data belonging to class i is as follows [35,36]:…”

Section: Single Water Source Identificationmentioning

confidence: 99%

Novel Method on Mixing Degree Quantification of Mine Water Sources: A Case Study

Li,

Fan,

Zhang

et al. 2024

Processes

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After a mine water inrush occurs, it is crucial to quickly identify the source of the water inrush and the key control area, and to formulate accurately efficient water control measures. According to the differences in water chemical characteristics of four aquifers in the Fenyuan coal mine, the concentrations of K+~Na+, Ca2+, Mg2+, Cl−, SO42−, and HCO3− were taken as water source identification indexes. A decision tree classification model based on the C4.5 algorithm was adopted to visualize the chemical characteristics of a single water source and extract rules, and intuitively obtained the discrimination conditions of a single water source with Mg2+, Ca2+, and Cl− as important variables in the decision tree: Mg2+ < 39.585 mg/L, Cl− < 516.338 mg/L and Mg2+ ≥ 39.585 mg/L, Ca2+ < 160.860 mg/L. Factor analysis and Fisher discriminant theory were used to eliminate the redundant ion variables, and the discriminant function equations of the two, three, and four types of mixed water sources were obtained successively in turn. This paper puts forward MSE, RMSE, and MAE as the evaluation indexes of the water source mixing degree calculation models and obtains the ranking of the pros and cons of the mixed water source mixing degree calculation models. The results show that the minimum inscribed circle analytical method is the optimal model for the calculation of the mixing degree of two types of water sources, and the MSE, RMSE, and MAE are 0.17%, 4.13%, and 4.13%, respectively. The minimum inscribed circle clustering method is the optimal model for the calculation of the mixing degree of three types of water sources, and the minimum distance method is the optimal model for the calculation of the mixing degree of four types of water sources. The method of mine water source identification based on the decision tree C4.5 algorithm and mixing degree calculation has the characteristics of a simple calculation process, high efficiency, objective accuracy, and low cost, which can provide a scientific basis for the development of stope water control measures.

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Section: Single Water Source Identificationmentioning

confidence: 99%

Novel Method on Mixing Degree Quantification of Mine Water Sources: A Case Study

Li,

Fan,

Zhang

et al. 2024

Processes

View full text Add to dashboard Cite

show abstract

“…Cho & Kurup [33] utilized a decision tree approach using two different tree models such as C4.5 and CART for the classification and dimensionality reduction of Electronic Nose (EN). Kaur, Singh, Garg & Harmanpreet [34] studied about classification algorithm for farm Decision Support System. Several classification algorithms used are limited search, ID3, CHAID, C4.5, improved C4.5 and One VS all decision tree and applied to one common data set of the crop with specified class.…”

Section: Literature Reviewmentioning

confidence: 99%