Taiga: performance optimization of the C4.5 decision tree construction algorithm

Yang, Yi; Chen, Wenguang

doi:10.1109/tst.2016.7536719

Cited by 27 publications

(17 citation statements)

References 12 publications

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“…First 10 results are shown in Table 3. It can be inferred that the RF model of station group is more accurate than SVR and C4.5 (decision tree using information gain as split criterion) [25], for it put less emphasis on temporality and output basing on different stations. SVR is not capable of the prediction of station group.…”

Section: ) Predictionmentioning

confidence: 99%

The Application of Improved Random Forest Algorithm on the Prediction of Electric Vehicle Charging Load

Xie

et al. 2018

Energies

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To cope with the increasing charging demand of electric vehicle (EV), this paper presents a forecasting method of EV charging load based on random forest algorithm (RF) and the load data of a single charging station. This method is completed by the classification and regression tree (CART) algorithm to realize short-term forecast for the station. At the same time, the prediction algorithm of the daily charging capacity of charging stations with different scales and locations is proposed. By combining the regression and classification algorithms, the effective learning of a large amount of historical charging data is completed. The characteristic data is divided from different aspects, realizing the establishment of RF and the effective prediction of fluctuate charging load. By analyzing the data of each charging station in Shenzhen from the aspect of time and space, the algorithm is put into practice. The application form of current data in the algorithm is determined, and the accuracy of the prediction algorithm is verified to be reliable and practical. It can provide a reference for both power suppliers and users through the prediction of charging load.

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Section: ) Predictionmentioning

confidence: 99%

The Application of Improved Random Forest Algorithm on the Prediction of Electric Vehicle Charging Load

Xie

et al. 2018

Energies

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“…The paper deals with decision tree algorithm [8]. Algorithm classifies map data into predefined group of class means data that may be …”

Section: Literature Surveymentioning

confidence: 99%

“…classified according to their gain entropy [8,9]. Then, higher weighted class goes on the root and then just lower than higher class further classified into another class [10,11].…”

Section: Ashish Modi Sharath Kumar J Muralidhar Amentioning

confidence: 99%

Employee Performance Appraisal System Based on Ranking and Reviews

Modi

Muralidhar

2017

Asian J Pharm Clin Res

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Objective: In many organizations, employee data have to be maintained and utilized for many purposes. Here, in this paper, we are going to use such data to calculate an employee's performance.Methods: This employee data may be converted into useful information using data mining techniques such as K-means and decisions tree. K-means is used to find the rank of the employee means that the employee may come under in his criteria. Decision tree is used to find the review of an employee means that the employee needs improvement or he/she meets expectation.Results: This algorithm when utilized can identify the top employee who can be considered for appraisal or the eligible candidates for promotion. Hence, these algorithms such as K-mean and decision tree that help to find best employees for any association and help us to take a good decision in less time. Conclusion:There are various factors which should be considered and are limited to this algorithm, so human intervention is required to consider those factors. However, ranking and appraisal are seen in many companies, and this algorithm will definitely identify the potential candidates.

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“…These data accurately reflect the actual purchase behavior of customers. Taking the actual marketing data of the enterprise as the training data sample collection for customer acquisition data mining, it can more accurately find the attribute characteristic of the target customer [9].…”

Section: Data Mining Modelmentioning

confidence: 99%

Study on Application of Data Mining in Customer Acquisition

Li¹,

Qin²,

Zhu³

et al. 2017

dtssehs

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Abstract. To identify target customers is one of keys for distributor to gain competitive advantage and reduce marketing costs. The objective of this study is to propose a customer acquisition model based on binary behavioral response and apply this model to customer acquisition. In this model, the training sample set D and its attribute set A are input into data mining algorithm f(D,A) to carry out binary classification calculation, the attributes corresponded to the "yes" response are the set of target customer attribute. According to the set of target customer attribute, a set of association rules are derived from the "yes" response behavior. The new feedback data is scanned with the obtained association rules to generate the target customer list. Employing RAINFOREST-CC as the data mining algorithm, this customer acquisition model has been applied to identify target customer for an automobile 4S store, and the result indicates that this model is effective.

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Taiga: performance optimization of the C4.5 decision tree construction algorithm

Cited by 27 publications

References 12 publications

The Application of Improved Random Forest Algorithm on the Prediction of Electric Vehicle Charging Load

The Application of Improved Random Forest Algorithm on the Prediction of Electric Vehicle Charging Load

Employee Performance Appraisal System Based on Ranking and Reviews

Study on Application of Data Mining in Customer Acquisition

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