English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The complex systems with edge computing require a huge amount ofmulti-feature data to extract appropriate insights for their decision making,so it is important to find a feasible feature selection method to improve thecomputational efficiency and save the resource consumption. In this paper, aquantum-based feature selection algorithm for the multi-classification problem,namely, QReliefF, is proposed, which can effectively reduce the complexity ofalgorithm and improve its computational efficiency. First, all features of eachsample are encoded into a quantum state by performing operations CMP and R_y,and then the amplitude estimation is applied to calculate the similaritybetween any two quantum states (i.e., two samples). According to thesimilarities, the Grover-Long method is utilized to find the nearest k neighborsamples, and then the weight vector is updated. After a certain number ofiterations through the above process, the desired features can be selected withregards to the final weight vector and the threshold {\tau}. Compared with theclassical ReliefF algorithm, our algorithm reduces the complexity of similaritycalculation from O(MN) to O(M), the complexity of finding the nearest neighborfrom O(M) to O(sqrt(M)), and resource consumption from O(MN) to O(MlogN).Meanwhile, compared with the quantum Relief algorithm, our algorithm issuperior in finding the nearest neighbor, reducing the complexity from O(M) toO(sqrt(M)). Finally, in order to verify the feasibility of our algorithm, asimulation experiment based on Rigetti with a simple example is performed.

Quantum-Based Feature Selection for Multi-classification Problem in Complex Systems with Edge Computing