# MIPART: A Partial Decision Tree-Based Method for Multiple-Instance Classification
## 4 Experimental Studies
### Experimental Results  
Table 3 presents the accuracy results for six prominent multiple-instance classification algorithms--Citation k-nearest neighbors (Citation-KNN), multi-instance boosting (MIBoost), multi-instance expectation-maximization diverse density (MIEMDD), multi-instance support vector machine (MISVM), multi-instance logistic regression (MILR), and  
Figure 3: Accuracy results of the MIPART method for varying values of the minNumObj. multi-instance tree inference (MITI)--across 12 benchmark datasets: Musk1, Musk2, Elephant, Fox, Tiger, mutagenesis3_atoms, mutagenesis3_bonds, mutagenesis3_chains, component, eastwest, function, and westeast. These algorithms, well-regarded for their effectiveness in multiple instance classification within the SimpleMI framework, serve as benchmarks for assessing our proposed method. To ensure a fair comparison, we re-implemented these methods under identical conditions, using the same datasets, parameters, and platform (WEKA) with standard configurations. On average, MIPART achieved an accuracy of 84.27% across these datasets, representing a 15% improvement over the aforementioned methods. This considerable enhancement highlights MIPART's adaptability across diverse multi-instance learning contexts, consistently outperforming other approaches. Additionally, we will compare our method to state-of-the-art methods from the literature in the \"Discussion\" section, using results reported by their authors on the same datasets to further validate MIPART's performance. In dataset-specific results, MIPART exhibited notable gains in challenging datasets like eastwest and westeast, where it reached an accuracy of 80%, surpassing other algorithms by over 30%. MIPART also performed exceptionally well on the function dataset, achieving 96.70% accuracy, an improvement of over 2% compared to the closest competitor, MITI, which achieved 94.45%. Additionally, MIPART's average accuracy of 84.27% across all datasets significantly outperformed the next best-performing method, MILR, which had an average accuracy of 74.56%, as well as MIEMDD and MISVM, which achieved average accuracies of 71.04% and 68.77%, respectively. This gap highlights a substantial improvement, with MIPART consistently leading across varied dataset complexities. Moreover, MIPART demonstrated resilience on complex datasets like Fox and component, where multiple instance classification can be particularly challenging due to data variability. On Fox, MIPART reached 64.50% accuracy, surpassing the best alternative method, MITI, which achieved 61%. For component, MIPART achieved an accuracy of 93.58%, significantly higher than MILR's 90.22% and the other methods, indicating MIPART's robustness even in high-variability conditions. These findings collectively suggest that MIPART is especially well-suited for multi-instance learning tasks that require high accuracy, reliable predictions, and adaptability across a range of complex and diverse datasets. Figure 4 presents the sensitivity values obtained for the MIPART method from experiments on multiple-sample learning datasets. The sensitivity metric, representing the true positive rate for each dataset, varies between 0 and 1. Results show that the highest sensitivity was achieved with the function dataset (0.97), followed by the _component_ dataset (0.94). The sensitivity values for the Musk1, Musk2, Tiger, Elephant, mutagenesis3_atoms,