## 1 Introduction
Classification accuracy of 93.84% was achieved for SS, amorphous intermetallic compounds, and 84.32% for BCC, FCC, and their mixed phases. [PERSON] et al. [25] utilized various feature selection methods to screen the feature set containing elemental alloy compositions, empirical HEA design parameters, metallurgy-informed alloy processing and postprocessing parameters, and other features. The accuracy of the obtained feature set exceeds 90% for alloy phase classification using various machine learning algorithms. [PERSON] et al. [26] used a genetic algorithm to screen among 70 features related to alloy phase classification. Eventually, the obtained features were fitted using SVM, which resulted in a classification accuracy of 88.7% for SS and non-SS and 91.3% for BCC, FCC, and their hybrid phases. Although these prediction methods are effective, the input features are mainly chosen from empirical parameters constructed by previous researchers. In fact, due to an unclear understanding of the alloy phase formation mechanism, there are still a large number of parameters that may affect the phase formation in HEAs, which makes it difficult to further optimize prediction models. Constructing a huge feature set plays an important role in discovering new features that can be used for alloy phase classification. In addition, a large number of single-phase SS were included in the past HEA data. This data property lowers the prediction accuracy in a few classes of phases due to an imbalance in phase classes when building data-driven models using ML. To solve this problem, many studies have used the random oversampling method. For example, [PERSON] et al. [27] used random oversampling on the dataset and trained the support vector machine, gradient boosting decision tree, multi-layer perceptron, and other algorithms to differentiate the SS phases. [PERSON] et al. [28] used a feature selection method based on feature importance and correlation to filter modeling features and finally found that K-Nearest Neighbors and Random Forest classifiers performed significantly better on the dataset after oversampling and principal component analysis (PCA) dimensionality reduction. However, these methods applied random oversampling to the model prior to training, which resulted in early data leakage and thus overestimation of model accuracy. Since the random oversampling method simply repeats for a few classes of data, it can cause the model to appear overfitted. Also, since most methods use greedy feature selection algorithms such as Embedded and forward/backward sequence selection algorithms in feature selection, this may result in the less optimal feature set being chosen as the final feature for modeling construction. Inaddition, feature extraction methods such as PCA should be avoided because they can change the distribution of features and make the model lose its physical interpretability. In this work, a huge feature set containing 145 features for exploring previously unused empirical parameters was constructed. The LightGBM algorithm for building classifiers that distinguish different phases was selected. Combining the two feature selection algorithms facilitates fast and accurate feature selection over huge feature sets. The optimal feature set, including nine features determined by global searching of the feature set, was determined. Finally, to address data imbalance and model overfitting issues, ensemble learning, SMOTE oversampling methods, and cost-sensitive methods were combined.