# MIPART: A Partial Decision Tree-Based Method for Multiple-Instance Classification
## 1 Introduction
Utilizing PART in the context of multi-instance learning has the potential to enhance classification outcomes by leveraging the distinct characteristics of bags and instances. However, the integration of PART with robust approaches to address multi-instance complexities remains largely unexplored in the literature. To complement the strengths of PART, the SimpleMI classifier [12] serves as a fundamental component for managing the complexity of bagged instances in multi-instance learning. SimpleMI operates by utilizing the relationships among instances within bags, enabling it to identify relevant instances that contribute to positive bag labels. This classifier typically employs a straightforward and efficient decision-making process, lacking the depth of more complex algorithms. The design of SimpleMI is particularly advantageous when addressing ambiguous labels, as it provides a solid foundation for more sophisticated approaches. However, its performance can be further enhanced through the integration of advanced techniques, which better capture the underlying data distribution and improve overall classification accuracy. One of the most effective advanced techniques in this realm is ensemble methods, which have gained prominence in machine learning for their ability to combine multiple models to achieve superior performance. Among these methods, bootstrap aggregating (Bagging) [13] stands out as a notable ensemble technique that decreases model variance and improves generalization. By training multiple models on different bootstrap samples of the training data, Bagging effectively mitigates the impact of noise and outliers. Each model contributes to the final prediction through averaging or majority voting, leading to a more stable and accurate overall result. This approach is particularly advantageous in multi-instance learning, where intra-bag diversity can complicate the classification task. By employing Bagging, models can leverage the strengths of individual learners while minimizing their weaknesses, thus enhancing the reliability of predictions in complex datasets. Building on the advantages offered by ensemble methods, this paper proposes the multi-instance partial decision tree (MIPART), presented as an advancement in the multiple-instance classification. MIPART offers a comprehensive approach that enhances the effectiveness of the PART algorithm in multi-instance scenarios while improving both accuracy and computational efficiency. By integrating PART within an ensemble framework and utilizing the SimpleMI classifier as the base learner, MIPART effectively manages the inherent variability and complexity within bags. This combination captures critical instance-level information that contributes to bag-level classification. Additionally, by employing the Bagging technique, MIPART mitigates overfitting, leading to more robust generalization across diverse datasets. Furthermore, the incorporation of PART not only provides intuitive insights into the decision-making process, but also facilitates the identification of the most influential instances within each bag, making it an invaluable tool for researchers in the field of machine learning. The main contributions of this study are listed as follows:  
* Proposing of a classification method: This study proposes the multi-instance partial decision tree (MIPART), a unique approach that incorporates the PART algorithm within a Bagging framework, leveraging the simple multi-instance classifier (SimpleMI) as its base. * Significant improvement in predictive performance: MIPART achieved an average accuracy of 84.27% across 12 real-world multi-instance datasets, demonstrating superior predictive capabilities compared to existing methods. * Outperformance of existing methods: The MIPART method outperformed counterpart techniques, including Citation-KNN, MIBoost, MIEMDD, MILR, MISVM, and MITI, achieving a 15% improvement in average accuracy across benchmark datasets. Additionally, it demonstrated a 4.7% average improvement over state-of-the-art methods.