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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

A classical decision tree is completely based on splitting measures, whichutilize the occurrence of random events in correspondence to its class labelsin order to optimally segregate datasets. However, the splitting measures arebased on greedy strategy, which leads to construction of an imbalanced tree andhence decreases the prediction accuracy of the classical decision treealgorithm. An intriguing approach is to utilize the foundational aspects ofquantum computing for enhancing decision tree algorithm. Therefore, in thiswork, we propose to use fidelity as a quantum splitting criterion to constructan efficient and balanced quantum decision tree. For this, we construct aquantum state using the occurrence of random events in a feature and itscorresponding class. The quantum state is further utilized to compute fidelityfor determining the splitting attribute among all features. Using numericalanalysis, our results clearly demonstrate that the proposed algorithmcooperatively ensures the construction of a balanced tree. We further comparedthe efficiency of our proposed quantum splitting criterion to differentclassical splitting criteria on balanced and imbalanced datasets. Oursimulation results show that the proposed splitting criterion exceeds allclassical splitting criteria for all possible evaluation metrics.

Quantum-inspired attribute selection algorithm: A Fidelity-based Quantum Decision Tree