## 5 Experiment Design
### Dataset and Data Preprocessing  
The experiments and result comparisons are based on five datasets from the UCI Machine Learning Repository [21] and the ADBench Repository [22]. These datasets present a diverse range of real-world domains and applications. The datasets are listed in Table 2.  
* The Taiwan bankruptcy dataset were collected from the Taiwan Economic Journal for the years 1999 to 2009. Company bankruptcy was defined based on the business regulations of the Taiwan Stock Exchange. * The Musk dataset describes a set of 102 molecules of which 39 are judged by human experts to be musks and the remaining 63 molecules are judged to be non-musks. The 166 features that describe these molecules depend upon the exact shape, or conformation, of the molecule. The label is either 0 if the record is non-musk and 1 if the record is musk. * The Parkinson's Disease (PD) dataset is composed of a range of biomedical voice measurements. The main aim of the data is to discriminate healthy people from those with PD, according to \"status\" column which is set to 0 for healthy and 1 for PD. * The Backdoor dataset has a hybrid of the real modern normal and the contemporary synthesized attack activities of the network traffic. It contains nine attack categories and normal network traffic. There are 49 features in the original UNSW-NB15 dataset developed by [PERSON] and [PERSON] [23]. [PERSON] et al. [22] synthetically create the Backdoor dataset based on the UNSW-NB15 dataset by adding more anomalies and irrelevant features up to more than 50% of the total input features. They follow and enrich the approach in [24] to generate realistic synthetic anomalies. * The Campaign dataset is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit. In our study, we used labeled datasets, where the minority class resembles outliers due to its rarity in high-dimensional data. The datasets are high-dimensional and imbalanced, making them suitable for evaluating the influence of feature selection in outlier detection applications. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Acronym** & **Description** \\ \hline Genetic Algorithms & GA & The basic Genetic Algorithm with the steps described in Section 3.1 \\ \hline Fixed value Quantum Genetic & FvQGA & The Quantum Genetic Algorithm proposed by [PERSON] and [PERSON] [11, 12] that employs a fixed value for the quantum rotation angle. \\ \hline Generation-based Quantum Genetic & GbQGA & The variant of Quantum Genetic Algorithm which has the quantum rotation angle decreases proportionately to the generation. \\ \hline Individual-based Quantum Genetic & IbQGA & The variant of Quantum Genetic Algorithm where each individual develops its quantum rotation angle based on its distance to the global best solution. \\ \hline Adaptive Feature-based Quantum & FbQGA & Our proposed method, details in Section 4, that formulates the quantum rotation angle for each feature based on its level of contribution. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Main characteristics of algorithms tested in the experiment. Each dataset is partitioned randomly into three subsets: 81% of the data serves as the training set, 9% is allocated for the validation set, and the remaining 10% is designated as the test set. To improve the optimization process and boost the algorithm's performance, the entire dataset was normalized to a range of 0 to 1. This scaling ensures that all features contribute equally to the model, preventing any single feature from dominating due to differences in scale. To address the issue of imbalanced classification, undersampling techniques were applied to modify the class distribution by removing instances from the majority class in the training dataset. Specifically, the NearMiss-1 undersampling method was used, which selects majority class instances based on their proximity to the three nearest instances of the minority class [29].