Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens

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

Multiple Instance Multiple Label Learning with Limited Supervision

In weak supervision learning, label information can be provided at different levels of granularity. For example, in multi-instance multi-label learning, samples are organized into bags and labels for each class are provided at the bag level. For small datasets, this approach offers means of reducing the labeling efforts. However, in the big-data order, even this mid-level labeling granularity can become prohibitively costly. Under this limited supervision, several labeling approaches can be considered to further reduce labeling costs. In semi-supervised learning, only a limited number of bags can be labeled meanwhile a large number of bags remain unlabeled. In partial- or incomplete-label learning, due to labeling policy or labeling challenges, only a subset of the classes can be labeled for each bag. This subset may be small or even an empty set. In active learning, a small number of class labels per bag are available and a limited number of the most informative labels from the unlabeled portion of data are queried in turn during the training phase to update the model efficiently. The goal is to achieve the best classifier in terms of performance with as least as possible number of available labels. All of the aforementioned approaches provide a practical solution for reducing labeling efforts under the multi-instance multi-label learning framework but introduce machine learning challenges both in terms of the required methodology as well as performance limitations for these methods. This work focuses on probabilistic models with exact/approximate but efficient inferences to leverage information from limited supervision data. In many cases, the proposed frameworks outperform, even significantly, the recently state-of-the-art approaches

Boosting for multi-graph classification

© 2014 IEEE. In this paper, we formulate a novel graph-based learning problem, multi-graph classification (MGC), which aims to learn a classifier from a set of labeled bags each containing a number of graphs inside the bag. A bag is labeled positive, if at least one graph in the bag is positive, and negative otherwise. Such a multi-graph representation can be used for many real-world applications, such as webpage classification, where a webpage can be regarded as a bag with texts and images inside the webpage being represented as graphs. This problem is a generalization of multi-instance learning (MIL) but with vital differences, mainly because instances in MIL share a common feature space whereas no feature is available to represent graphs in a multi-graph bag. To solve the problem, we propose a boosting based multi-graph classification framework (bMGC). Given a set of labeled multi-graph bags, bMGC employs dynamic weight adjustment at both bag- and graph-levels to select one subgraph in each iteration as a weak classifier. In each iteration, bag and graph weights are adjusted such that an incorrectly classified bag will receive a higher weight because its predicted bag label conflicts to the genuine label, whereas an incorrectly classified graph will receive a lower weight value if the graph is in a positive bag (or a higher weight if the graph is in a negative bag). Accordingly, bMGC is able to differentiate graphs in positive and negative bags to derive effective classifiers to form a boosting model for MGC. Experiments and comparisons on real-world multi-graph learning tasks demonstrate the algorithm performance

Multi-graph learning

University of Technology Sydney. Faculty of Engineering and Information Technology.Multi-instance learning (MIL) is a special learning task where labels are only available for a bag of instances. Although MIL has been used for many applications, existing MIL algorithms cannot handle complex data objects, and all require that instances inside each bag are represented as feature vectors (e.g. being represented in an instance-feature format). In reality, many real-world objects are inherently complicated, and an object can be represented as multiple instances with dependency structures (i.e. graphs). Such dependency allows relationships between objects to play important roles, which, unfortunately, remain unaddressed in traditional instance-feature representations. Motivated by the challenges, this thesis formulates a new multi-graph learning paradigm for representing and classifying complicated objects. With the proposed multi-graph representation, the thesis systematically addresses several key learning tasks, including Multi-Graph Learning: A graph bag contains one or multiple graphs, and each bag is labeled as either positive or negative. The aim of multi-graph learning is to build a learning model from a number of labeled training bags to predict previously unseen bags with maximum accuracy. To solve the problem, we propose two types of approaches: 1) Multi-Graph Feature based Learning (gMGFL) algorithm that explores and selects an optimal set of subgraphs as features to transfer each bag into a single instance for further learning; and 2) Boosting based Multi-Graph Classification framework (bMGC), which employs dynamic weight adjustment, at both graph- and bag-levels, to select one subgraph in each iteration to form a set of weak graph classifiers. Multi-Instance Multi-Graph learning: A bag contains a number of instances and graphs in pairs, and the learning objective is to derive classification models from labeled bags, containing both instances and graphs, to predict previously unseen bags with maximum accuracy. In the thesis, we propose a Dual Embedding Multi-Instance Multi-Graph Learning (DE-MIMG) algorithm, which employs a dual embedding learning approach to (1) embed instance distributions into the informative subgraphs discovery process, and (2) embed discovered subgraphs into the instance feature selection process. Positive and Unlabeled Multi-Graph Learning: The training set only contains positive and unlabeled bags, where labels are only available for bags but not for individual graphs inside the bag. This problem setting raises significant challenges because bag-of-graph setting does not have features available to directly represent graph data, and no negative bags exits for deriving discriminative classification models. To solve the challenge, we propose a puMGL learning framework which relies on two iteratively combined processes: (1) deriving features to represent graphs for learning; and (2) deriving discriminative models with only positive and unlabeled graph bags. Multi-Graph-View Learning: A multi-graph-view model utilizes graphs constructed from multiple graph-views to represent an object. In our research, we formulate a new multi-graph-view learning task for graph classification, where each object to be classified is represented graphs under multi-graph-view. To solve the problem, we propose a Cross Graph-View Subgraph Feature based Learning (gCGVFL) algorithm that explores an optimal set of subgraph features cross multiple graph-views. In addition, a bag based multi-graph model is further used to relax the labeling by only requiring one label for each graph bag, which corresponds to one object. For learning classification models, we propose a multi-graph-view bag learning algorithm (MGVBL), to explore subgraphs from multiple graph-views for learning. Experiments on real-world data validate and demonstrate the performance of proposed methods for classifying complicated objects using multi-graph learning