# MIPART: A Partial Decision Tree-Based Method for Multiple-Instance Classification
## 2 Related Works  
Multi-instance learning (MIL) has found widespread success across various domains, including image classification [14; 15; 16], drug discovery [17; 18], healthcare [19; 20], bioinformatics [21; 22], text and document classification [23; 24; 25; 26; 27], human activity recognition [28; 29], education [30], and security [31]. In image classification, [PERSON] et al. [14] introduce MILES, an approach to multi-instance learning. MILES maps each bag to a feature space based on the similarity of examples within the training bags, then applies SVM to identify key features. In their study [15], [PERSON] and [PERSON] expand on this by presenting DD-SVM, a MIL framework for both image classification and drug activity prediction. This framework treats images as bags containing multiple instances (regions from image segmentation), with bag labels assigned based on criteria met by certain instances. Another approach [16] considers image classification as a MIL problem, incorporating asymmetric loss functions to differentially penalize false negatives and false positives, improving the handling of misclassifications. In drug discovery, MIL [17] is used to predict the binding activity of molecules to receptors by evaluating the activity of molecule groups (bags) as a whole, rather than focusing on individual parts. This approach helps manage cases where individual molecular activity may be noisy or ambiguous, while also reducing the cost associated with labeling complex datasets [18]. MIL is also valuable in healthcare applications. [PERSON] et al. [19] emphasize its importance in early screening and diagnosis of conditions like breast cancer, where early benign lesions in mammograms are challenging to distinguish from normal tissue. They propose the Sequential Multiple-Instance Learning (OMIL) method, which labels only the sample set and models the sequential class information of bags and their contained samples. In [20], [PERSON] et al. developed a MIL-based framework for [PERSON]'s disease evaluation, utilizing wearable sensor data collected from 83 individuals in real-life settings. Their MIL framework addresses the challenge of weak interpretability often encountered in practical scenarios. In bioinformatics, MIL is particularly useful for classifying biological sequences, such as DNA pairs and amino acids [21]. In this context, the objective was to determine whether a protein performs a specific function. Protein sequences are treated as collections of subsequences or motifs, often created by sliding a window across the sequence to generate partial bags. It is believed that only one of these subsequences influences the behavior of the entire protein or DNA sequence [22]. Document classification was one of the earliest applications of MIL [23]. Since documents often contain texts on various topics, they can be modeled as bags, with passages, paragraphs, phrases, or words serving as instances. MIL enables document classification guided by the inclusion of pertinent information without requiring section-by-section labeling [24; 25; 26; 27]. In human activity recognition, [28] examined the impact of multi-instance representation on data collected from various wearable sensors. This research assesses the effectiveness of the SimpleMI and MIWrapper MIL algorithms in recognizing human activities. Their presented HAR-MIL technique proved high accuracy across 50 datasets based on sensor location and participant activity. In another study [29], MIL algorithms were applied to a dataset collected from eight users across two different activity combinations, with the MIML-kNN method achieving a high average precision rate. In the field of education, researchers [30] applied MIL to predict student performance, finding that the MIL approach effectively addressed challenges that traditional methods faced. For security, [PERSON] et al. [31] utilized MIL for malware classification.