基于遗传算法的决策树ml验证系统灵活特征约简

IF 1.3 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS

IET Networks Pub Date : 2022-10-14 DOI:10.1109/IET-ICETA56553.2022.9971631

Xin-Yu Shih, Yao Lu

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引用次数: 0

摘要

本文提出了一种系统的基于遗传算法的特征约简方法。它具有基于5元组参数调整的高设计灵活性。用户可以自行决定这5个参数，以满足关注精度或减少特征量的需求。采用不同数据集的决策树模型对算法进行了验证。对于数据集，ala，特征数量从123个减少到53个，而准确率性能提高了4.2%。此外，对于其他数据集，最大准确率损失不超过3.1%，特征约简率达到41.9%。它的优点是提供了精度和减少特征量之间的设计权衡。

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Systematic and Flexible Genetic-Algorithm-Based Feature Reduction for Decision Tree ML-Validation

In this paper, we propose a systematic genetic-algorithm-based feature reduction method. It has a high design flexibility based on 5-tuple parameter adjustment. The users can decide these 5 parameters to satisfy the demands of making the focus on accuracy or reduced feature amount. The proposed algorithm is verified by decision-tree models with different data sets. As for the data set, ala, the number of features is reduced from 123 to 53 while the accuracy performance has an increase of 4.2%. In addition, for other data sets, the maximum accuracy loss is no more than 3.1% while the feature reduction ratio achieves 41.9%. Its advantage is to provide a design trade-off between accuracy and reduced feature amount.

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来源期刊

IET Networks COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

5.00

自引率

0.00%

发文量

审稿时长

33 weeks

期刊介绍： IET Networks covers the fundamental developments and advancing methodologies to achieve higher performance, optimized and dependable future networks. IET Networks is particularly interested in new ideas and superior solutions to the known and arising technological development bottlenecks at all levels of networking such as topologies, protocols, routing, relaying and resource-allocation for more efficient and more reliable provision of network services. Topics include, but are not limited to: Network Architecture, Design and Planning, Network Protocol, Software, Analysis, Simulation and Experiment, Network Technologies, Applications and Services, Network Security, Operation and Management.