# MIPART: A Partial Decision Tree-Based Method for Multiple-Instance Classification
## 4 Experimental Studies
### Experiment Details  
This paper proposes the MIPART method, which is specifically designed for multiple-instance classification problems. This method integrates visions from the SimpleMI approach and PART classifier within the Bagging ensemble framework. The potency of the MIPART method was established through validation on 12 reference multi-instance datasets, including Musk1, Musk2, Elephant, Fox, Tiger, mutagenesis3_atoms, mutagenesis3_bonds, mutagenesis3_chains, component, eastwest, function, and westeast. The MIPART method utilized the Weka open-source data mining library [45]. The experiments were conducted on a standard machine (Intel(R) Core(TM) i7-4720 HQ CPU, 2.60 GHz, 16.00 GB RAM). Weka version 3.8.6 and Java programming language were used. Additionally, the selected hyperparameters for optimal performance are as follows:  
* Bagging parameters were set with \"bagSizePercent\" at 100, indicating that each bootstrap sample in the ensemble contains 100% of the training data. The \"batchSize\" was also set to 100, controlling the number of instances processed per batch for batch learning. The primary classifier for bagging was set to SimpleMI, an essential choice for multi-instance learning. For the ensemble, the \"numIterations\" was set to 100, meaning that 100 iterations or bags were created to achieve the variance reduction benefits of bagging. Also, the \"numExecutionSlots\" parameter was set to maximize computational efficiency, allowing parallel processing across available CPU cores to speed up model training. * Within the SimpleMI classifier, the \"batchSize\" parameter was set to 100, ensuring consistent batch processing across different stages and facilitating memory management. The \"classifier\" chosen was the PART algorithm for constructing partial decision trees and converting them into interpretable rules with a focus on high-information nodes. The \"transformMethod\" parameter was also configured to optimize data reduction, aligning the multi-instance data format with the classifier's requirements and enhancing both efficiency and accuracy. These carefully selected settings allowed for a streamlined and effective implementation of SimpleMI with the PART algorithm. * The PART classifier, essential to MIPART's interpretability, included key hyperparameters that balanced model complexity and performance. The \"minNumObj\" parameter was set to 2, establishing a minimum of two instances per leaf for rule generation. The \"confidenceFactor\", set to 0.25, controlled the pruning threshold, allowing PART to reduce overfitting by removing less informative branches. Furthermore, the \"numFolds\" was set to 3 for internal cross-validation during pruning, while the \"unpruned\" option was set to False, indicating that pruning was applied to simplify the model. These configurations played a significant role in enhancing the reliability of the multiple instance classification results. It is noteworthy to regard that, in our study, we conducted additional experiments to determine the optimal hyperparameter values for our setup. Specifically, for the BatchSize parameter, we explored values ranging from 10 to 150 in steps of 10, in addition to the default value of 100. Since the results remained consistent across all tested values, we decided to retain the default value of 100 for BatchSize in our final experiments. For the confidenceFactor parameter, which takes values between 0 and 1, we tested values of 0.50, 0.75, and 0.99, alongside the default value of 0.25. The results obtained with these different values were very similar to those achieved using the default value of 0.25. As a result, we chose to use the default value of 0.25 for confidenceFactor in our experiments. Similarly, for the minNumObj parameter, we experimented with values ranging from 2 to 15, in steps of 1, including 2 as its default value. As shown in Figure 3, the accuracy values of the model decreased as we increased the value of minNumObj, with the highest accuracy observed at the default value of 2.