Federated Multi-Instance Multi-Label Learning Based on Label Richness and Balance

In this paper, we formalize multi-instance multi-label learning, where each training example is associated with not only multiple instances but also multiple class labels. Such a problem can occur in many real-world tasks, e.g. an image usually contains multiple patches each of which can be described by a feature vector, and the image can belong to multiple categories since its semantics can be recognized in different ways. We analyze the relationship between multi-instance multi-label learning and the learning frameworks of traditional supervised learning, multi-instance learning and multi-label learning. Then, we propose the MIMLBOOST and MIMLSVM algorithms which achieve good performance in an application to scene classification.

Multi-instance multi-label distance metric learning for genome-wide protein function prediction

Recently, Multi Instance Multi Label (MIML) learning has attracted the attention of researchers in which an example not only belongs to multiple instances but also associated with multiple class labels. This study proposes a novel multi-instance multi-label twin support vector machine (MIMLTWSVM) classifier by extending the recently proposed binary twin support vector machine (TWSVM) classifier. MIMLTWSVM classifier involves two steps-(1) the problem of multi-instance multi-label has been converted to single instance multi label learning in the first step and (2) in the second step, the derived problem is solved by using multi-label twin support vector machine classifier. The involved Quadratic Programming Problems (QPPs) of proposed classifier has been solved by Successive Over-Relaxation (SOR) technique to speed up the training procedure. The experiment has been conducted on two MIML benchmark datasets-Scene and Reuters. The experimental results demonstrate the superiority of the proposed classifier over several existing state-of-the-art MIML classifiers such as MIMLSVM, MIMLRBF, MIMLBOOST, MIML-kNN and M <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> MIML.

W44. OXTR DNA METHYLATION DIFFERENTIATES MEN ON THE OBESITY SPECTRUM WITH AND WITHOUT AN EATING DISORDER

Nature often brings several domains together to form multidomain and multifunctional proteins with a vast number of possibilities. In our previous study, we disclosed that the protein function prediction problem is naturally and inherently Multi-Instance Multilabel (MIML) learning tasks. Automated protein function prediction is typically implemented under the assumption that the functions of labeled proteins are complete; that is, there are no missing labels. In contrast, in practice just a subset of the functions of a protein are known, and whether this protein has other functions is unknown. It is evident that protein function prediction tasks suffer from weak-label problem; thus protein function prediction with incomplete annotation matches well with the MIML with weak-label learning framework. In this paper, we have applied the state-of-the-art MIML with weak-label learning algorithm MIMLwel for predicting protein functions in two typical real-world electricigens organisms which have been widely used in microbial fuel cells (MFCs) researches. Our experimental results validate the effectiveness of MIMLwel algorithm in predicting protein functions with incomplete annotation.

Efficacy of utilizing a hybrid algorithmic method in enhancing the functionality of multi-instance multi-label radial basis function neural networks

Classifying natural scenes into semantic categories has always been a challenging task. So far, many works in this field are primarily intended for single label classification, where each scene example is represented as a single instance vector. The multi-instance multi-label (MIML) learning framework proposed by Z.H. Zhou et al. [1] provides a new solution to the problem of scene classification in a different way. In this paper, we propose a novel scene classification method based on pLSA-based semantic bag generator and MIML learning framework. Under the framework of MIML learning, we introduce the mechanism that transfers an image into a set of instances through the pLSA-based bag generator. Experiments show that our approach achieves better classification performance comparing with the previous work.

Multi-instance multi-label learning(MIML) has been successfully applied into many real-world applications. Along with the enhancing of the expressive power, the cost of labelling a MIML example increases significantly. And thus it becomes an important task to train an effective MIML model with as few labelled examples as possible. Active learning, which actively selects the most valuable data to query their labels, is a main approach to reducing labeling cost. Existing active methods achieved great success in traditional learning tasks, but cannot be directly applied to MIML problems. In this paper, we propose a MIML active learning algorithm, which exploits diversity and uncertainty in both the input and output space to query the most valuable information. This algorithm designs a novel query strategy for MIML objects specifically and acquires more precise information from the oracle without addition cost. Based on the queried information, the MIML model is then effectively trained by simultaneously optimizing the relative rank among instances and labels.

Constrained instance clustering in multi-instance multi-label learning

Multi-instance multi-label learning (MIML) is a framework in machine learning in which each object is represented by multiple instances and associated with multiple labels. This relatively new approach has achieved success in various applications, particularly those involving learning from complex objects. Because of the complexity of MIML, the cost of data labeling increases drastically along with the improvement of the model performance. In this paper, we introduce a MIML active learning approach to reduce the labeling costs of MIML data without compromising the model performance. Based on a query strategy, we select and request from the Oracle the label set of the most informative object. Our approach is formulated in a pool-based scenario and uses Miml-\(k\) nn as the base classifier. This classifier for MIML is based on the \(k\)-Nearest Neighbor algorithm and has achieved superior performance in different data domains. We proposed novel query strategies and also implemented previously used query strategies for MIML learning. Finally, we conducted an experimental evaluation on various benchmark datasets. We demonstrate that these approaches can achieve significantly improved results than without active selection for all datasets on various evaluation criteria.

Tag services have recently become one of the most popular Internet services on the World Wide Web. Due to the fact that a Web page can be associate with multiple tags, previous research on tag recommendation mainly focuses on improving its accuracy or efficiency through multi-label learning algorithms. However, as a Web page can also be split into multiple sections and be represented as a bag of instances, multi-instance multi-label learning framework should fit this problem better. In this paper, we improve the performance of tag suggestion by using multi-instance multi-label learning. Each Web page is divided into a bag of instances. The experiments on real-word data from delicious suggest that our framework has better performance than traditional multi-label learning methods on the task of tag recommendation.

EM3NL (end-to-end multi-view multi-instance multi-label learning with new labels

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the vast majority of proteins can only be annotated computationally. Nature often brings several domains together to form multi-domain and multi-functional proteins with a vast number of possibilities, and each domain may fulfill its own function independently or in a concerted manner with its neighbors. Thus, it is evident that the protein function prediction problem is naturally and inherently Multi-Instance Multi-Label (MIML) learning tasks. Based on the state-of-the-art MIML algorithm MIMLNN, we propose a novel ensemble MIML learning framework EnMIMLNN and design three algorithms for this task by combining the advantage of three kinds of Hausdorff distance metrics. Experiments on seven real-world organisms covering the biological three-domain system, i.e., archaea, bacteria, and eukaryote, show that the EnMIMLNN algorithms are superior to most state-of-the-art MIML and Multi-Label learning algorithms.

Nearest neighbor-based approaches for multi-instance multi-label classification

Existing studies for automatic waveform recognition of overlapping signals have mostly been conducted in a supervised manner. Although demonstrating superior performance in recent years, supervised methods rely heavily on sufficient labeled samples, rely but the acquisition of annotated data is expensive, time-consuming, and sometimes rely infeasible. This shortage drives the need for semi-supervised learning methods, where the unlabeled samples can be fully exploited in the training stage. In addition, multi-instance multi-label (MIML) learning is essentially another weakly supervised learning protocol, and precisely fits the form of the time-frequency images TFIs obtained from the transformation of overlapping signals. In this paper, delving into the MIML learning problem, we leverage the advantage of adversarial training to formulate an effective algorithm MIML-GAN, which is tailored to the MIML problem of overlapping signal waveform recognition. After feeding the TFIs into the network, MIML-GAN approximates the distribution of the training data using the adversarial learning principle. Subsequently, the baglevel prediction can be derived from the instance-level prediction upon the MIML discriminator through adaptive threshold calibration. Specifically, we elaborately studied the global optimality of the MIML-GAN objective function, and extensive simulations are carried out with overlapping signal dataset, validating the ascendancy of the proposed method. Comparative experiments demonstrate that the proposed algorithm possesses promising feature representation capability, and outperforms the existing semi-supervised and supervised signal waveform recognition approaches.