Binary Classifier Inspired by Quantum Theory
This addresses the need for better prediction models in domains like biomedical and agricultural fields by replacing classical theories with quantum theory, though it appears incremental as it builds on existing quantum-inspired approaches.
The paper tackles the problem of improving prediction in machine learning by proposing a binary classifier inspired by quantum theory, which outperforms state-of-the-art classification in terms of recall for every category.
Machine Learning (ML) helps us to recognize patterns from raw data. ML is used in numerous domains i.e. biomedical, agricultural, food technology, etc. Despite recent technological advancements, there is still room for substantial improvement in prediction. Current ML models are based on classical theories of probability and statistics, which can now be replaced by Quantum Theory (QT) with the aim of improving the effectiveness of ML. In this paper, we propose the Binary Classifier Inspired by Quantum Theory (BCIQT) model, which outperforms the state of the art classification in terms of recall for every category.