# Improving Multi-Instance Multi-Label Learning by Extreme Learning Machine
## 1 Introduction  
When utilizing machine learning to solve practical problems, we often consider an object as a feature vector. Then, we get an instance of the object. Further, associating the instance with a specific class label of the object, we obtain an example. Given a large collection of examples, the task is to get a function mapping from the instance space to the label space. We expect that the learned function can predict the labels of unseen instances correctly. However, in some applications, a real-world object is often ambiguous, which consists of multiple instances and corresponds to multiple different labels simultaneously. For example, an image usually contains multiple patches each represented by an instance, while in image classification, such an image can belong to several classes simultaneously, e.g., an image can belong to mountains, as well as Africa [1]; another example is text categorization [1], where a document usually contains multiple sections each of which can be represented as an instance, and the document can be regarded as belonging to different categories if it were viewed from different aspects, e.g., a document can be categorized as a scientific novel, [PERSON]'s writing or even books on traveling. The MIML (Multi-instance Multi-label) problem also arises in the protein function prediction task [2]. A domain is a distinct functional and structural unit of a protein. A multi-functional protein often consists of several domains, each fulfilling its own function independently. Taking a protein as an object, a domain as an instance and each biological function as a label, the protein function prediction problem exactly matches the MIML learning task. In this context, multi-instance multi-label learning was proposed [1]. Similar to the other two multi-learning frameworks, _i.e._, multi-instance learning (MIL) [3] and multi-label learning (MLL) [4],the MIML learning framework also results from the ambiguity in representing the real-world objects. Differently, more difficult than two other multi-learning frameworks, MIML studies the ambiguity in terms of both the input space (_i.e.,_ instance space) and the output space (_i.e.,_ label space), while MIL just studies the ambiguity in the input space and MLL just the ambiguity in the output space, respectively. In [1], [PERSON] _et al._ proposed a degeneration strategy-based framework for MIML, which consists of two phases. First, the MIML problem is degenerated into the single-instance multi-label (SIML) problem through a specific clustering process; second, the SIML problem is decomposed into a multiple independent binary classification (_i.e.,_ single-instance single-label) problem using Support Vector Machine (SVM) as the classifiers builder. This two-phase framework has been successfully applied to many real-world applications and has been shown to be effective [5]. However, it could be further improved if the following drawbacks are tackled. On one hand, the clustering process in the first phase requires a user-specific parameter for the number of clusters. Unfortunately, it is often difficult to determine the correct number of clusters in advance. The incorrect number of clusters may affect the accuracy of the learning algorithm; on the other hand, SIML is degenerated into single-instance single-label learning (SISL) (_i.e.,_ single instance, single label) in the second phase, as this will increase the volume of data to be handled and thus burden the classifier building. Utilizing SVM as the classifier builder in this phase may suffer from a high computational cost and require a number of parameters to be optimized. In this paper, we propose to enhance the two-phase framework by tackling the two above issues and make the following contributions: (1) We utilize extreme learning machine (ELM) [6] instead of SVM to improve the efficiency of the two-phase framework. To our best knowledge, we are the first to utilize ELM in the MIML problem and to conduct the comparison of ELM and SVM on MIML.