# MIPART: A Partial Decision Tree-Based Method for Multiple-Instance Classification
## 6 Conclusions and Future Works  
This study proposed the multi-instance partial decision tree (MIPART) as a significant advancement in addressing the challenges associated with multi-instance learning. By integrating a Bagging ensemble with the partial decision tree (PART) algorithm and utilizing SimpleMI as the base classifier, MIPART effectively combines interpretability with strong classification capabilities. The use of the partial decision tree in MIPART is crucial because it allows for the effective handling of uncertain or ambiguous labeling within multi-instance learning, where instances in a bag are not individually labeled but the bag as a whole has a label. The PART algorithm's ability to generate interpretable models that focus on the most informative features, even when dealing with complex data structures, makes it particularly well-suited for multi-instance classification. On the other hand, SimpleMI is notable for its efficiency and simplicity, as it consolidates multiple instances within a bag into a single representative value by calculating the arithmetic mean. Evaluated across 12 diverse multi-instance datasets, MIPART achieved an impressive average accuracy of 84.27%. Notably, this represents a 15% improvement over similar multiple instance classification methods, including Citation-KNN, MIBoost, MIEMDD, MILR, MISVM, and MITI. These findings demonstrate MIPART's robust ability to navigate the complexities inherent in multi-instance data, positioning it as a high-performance tool for applications that require precise classification in intricate data structures. The substantial accuracy gains achieved by MIPART underscore its potential to contribute significantly to the field of multiple instance classification. Furthermore, the statistical significance of these performance improvements was confirmed through non-parametric tests, including Friedman aligned ranks and Wilcoxon signed-rank tests, with \(p\)-values below the 0.05 significance threshold, ensuring the reliability of the results. Looking ahead, future research will focus on several promising avenues to extend the MIPART method's perspectives. One significant direction is the integration of real-time data processing capabilities, allowing MIPART to operate in dynamic environments where timely \begin{table}
\begin{tabular}{c c c} \hline
**Dataset** & **Method** & **Accuracy** \\ \hline \multirow{8}{*}{Elephant} & SimpleMi & 73.60 \\  & MiWrapper & 84.30 \\  & TLC & 82.50 \\  & MiAC\({}_{\text{i}}\) & 85.30 \\  & MiAC\({}_{\text{e}}\) & 81.90 \\  & MiENC & 85.80 \\  & NoEncode & 83.70 \\ \cline{2-3}  & **Proposed** (MIPART) & **86.50** \\ \hline \multirow{8}{*}{Fox} & SimpleMi & 55.00 \\  & MiWrapper & 59.30 \\  & TLC & 62.70 \\  & MiAC\({}_{\text{i}}\) & 59.30 \\  & MiAC\({}_{\text{e}}\) & 57.80 \\  & MiENC & 60.60 \\  & NoEncode & 58.10 \\ \cline{2-3}  & **Proposed** (MIPART) & **64.50** \\ \hline \multirow{8}{*}{Tiger} & SimpleMi & 76.60 \\  & MiWrapper & 78.60 \\  & TLC & 75.20 \\  & MiAC\({}_{\text{i}}\) & 82.10 \\  & MiAC\({}_{\text{e}}\) & 75.20 \\  & MiENC & 80.40 \\  & NoEncode & 79.50 \\ \cline{2-3}  & **Proposed** (MIPART) & **82.50** \\ \hline \end{tabular}
\end{table} 
Table 5: _Cont._decisions are critical. Additionally, exploring the development of an automated pipeline for data preprocessing, model deployment, and adapting the method for streaming data will efficiently facilitate the application of MIPART in various practical scenarios, improving user experience and accessibility. Finally, the creation of user-friendly interfaces, such as web or mobile applications, will be prioritized to broaden MIPART's accessibility for researchers and industry practitioners alike. By making the tool more readily available, we aim to foster its adoption and encourage innovative applications across diverse fields. This research received no external funding. Not applicable.