A Hybrid Genetic Algorithm With Wrapper-Embedded Approaches for Feature Selection

Li, Xiaoying; Liang, Yong; Wang, Qianqian; Yang, Ziyi; Ye, Han-Shuo

doi:10.1109/access.2018.2818682

Cited by 112 publications

(76 citation statements)

References 74 publications

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“…Compared with this study, the cost-effective GA method is very unstable in response time for each query. The main reason is that GA is a random search process and the number of generations it performs is not fixed [120]. This means that users are less likely to predict when search results will be generated.…”

Section: ) Discussion On the Difference Between This Study And The Cmentioning

confidence: 99%

A Study of Optimizing Search Engine Results Through User Interaction

Chen

2020

IEEE Access

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In this paper, we propose a method for optimizing search engine results based on user interaction. This method generates different search results mainly through the user's operation on different search topics. There are two main differences between our method and the traditional personalized search method: personal privacy and storage space. First, traditional personal search methods need to record search and click records for individual users. However, these records have great personal privacy issues for users; especially in recent years, there have been personal privacy leaks that occurred in many large online companies (such as Facebook and Yahoo). Secondly, because different users need to record their own search records, the size of the storage space is closely related to the number of users and the amount of search records stored. However, the sum of individual users' search and click records is a huge storage space in today's Internet environment. To avoid these two issues for traditional personalized search methods, this study proposes a storage-free approach based on individual users operating on different search topics. In general, in addition to avoiding personal privacy and storage space issues, our method can also achieve optimal linear time in generating personalized search results. INDEX TERMS Document clustering, machine learning, natural language processing, personalized topic search, user log.

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Section: ) Discussion On the Difference Between This Study And The Cmentioning

confidence: 99%

A Study of Optimizing Search Engine Results Through User Interaction

Chen

2020

IEEE Access

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“…FS methods aim to identify and choose a subset of features to describe the data concept effectively. Simultaneously, FS can reduce the effects of noise and unrelated attributes to yield a good prediction of data class [21], [32], [33]. Traffic identification can greatly benefit in terms of accuracy and other performance metrics by utilizing the most significant features [34].…”

Section: B the Use Of Feature Selectionmentioning

confidence: 99%

Edge Computing Intelligence Using Robust Feature Selection for Network Traffic Classification in Internet-of-Things

et al. 2020

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“…Figure 4 presents the wrapper descriptor selection principle which works by generating candidate subsets from all available descriptors and then evaluating each subset with a classification algorithm. Several strategies for generating subsets can be listed, each with their own advantages and disadvantages, including exhaustive search (brute force [26], branch and bound [27]), heuristic (hill climbing, best first search) and meta-heuristic (genetic algorithm and particle swarm) [28] [29]. Compared to the other methods, the exhaustive search method guarantees that the best subset of features will be found.…”

Section: B Wrapper Descriptor Selectionmentioning

confidence: 99%