Reducing the Depth of ID3 Algorithm by Combining Values from Neighboring Important Attributes

Kraidech, Suratchanan; Jearanaitanakij, Kietikul

doi:10.1109/icsec.2018.8712788

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The decision tree technique is widely used in data analysis and prediction [14][15][16][17][18][19][20][21]. For example, in [16], the C4.5 decision tree algorithm is applied to achieve precision marketing prediction.…”

Section: Decision Tree-based Classificationmentioning

confidence: 99%

“…A service decision tree-based post-pruning prediction approach is proposed to classify the services into the corresponding reliability level after discretizing the continuous attribute of services in service-oriented computing [18]. The ID3 is one of the standard algorithms for the decision tree learning process, which calculates the entropy to select the condition attributes [19][20][21].…”

Section: Decision Tree-based Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Blockchain-Based Decision Tree Classification in Distributed Networks

Yu¹,

Qiao²,

Tang³

et al. 2021

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

In a distributed system such as Internet of things, the data volume from each node may be limited. Such limited data volume may constrain the performance of the machine learning classification model. How to effectively improve the performance of the classification in a distributed system has been a challenging problem in the field of data mining. Sharing data in the distributed network can enlarge the training data volume and improve the machine learning classification model's accuracy. In this work, we take data sharing and the quality of shared data into consideration and propose an efficient Blockchain-based ID3 Decision Tree Classification (BIDTC) framework for distributed networks. The proposed BIDTC takes advantage of three techniques: blockchain-based ID3 decision tree, enhanced homomorphic encryption, and stimulation smart contract to conduct classification while effectively considering the data privacy and the value of user data. BIDTC employs the data federation scheme based on homomorphic encryption and blockchain to achieve more training data sharing without sacrificing data privacy. Meanwhile, smart contracts are integrated into BIDTC to incentivize users to share more high-quality data. Our extensive experiments have demonstrated that the proposed BIDTC significantly outperforms existing schemes in constructed consortium blockchain networks.

show abstract