Multi-instance Multi-label Learning Algorithms Research Articles

A multi-instance multi-label learning algorithm based on radial basis functions and multi-objective particle swarm optimization

Radial basis function (RBF) neural networks for Multi-Instance Multi-Label (MIML) directly can exploit the connections between instances and labels so that they can preserve useful prior information, but they only adopt Gaussian radial basis function as their RBF whose parameters are difficult to determine. In this paper, parameters can be obtained by multi-objective optimization methods with multi performance measures treated as objectives, specifically, parameter estimation of different RBFs by an improved multi-objective particle swarm optimization (MOPSO) is proposed where Recall rate and Precision rate are chosen to obtain the most desirable Pareto optimal solution set. Furthermore, share-learning factor is proposed to modify the particle velocity in standard MOPSO to improve the global search ability and group cooperative ability. It is experimentally demonstrated that the proposed method can estimate the reliable parameters of different RBFs, and it is also very competitive with the state of art MIML methods.

Towards Discovering What Patterns Trigger What Labels

In many real applications, especially those involving data objects with complicated semantics, it is generally desirable to discover the relation between patterns in the input space and labels corresponding to different semantics in the output space. This task becomes feasible with MIML (Multi-Instance Multi-Label learning), a recently developed learning framework, where each data object is represented by multiple instances and is allowed to be associated with multiple labels simultaneously. In this paper, we propose KISAR, an MIML algorithm that is able to discover what instances trigger what labels. By considering the fact that highly relevant labels usually share some patterns, we develop a convex optimization formulation and provide an alternating optimization solution. Experiments show that KISAR is able to discover reasonable relations between input patterns and output labels, and achieves performances that are highly competitive with many state-of-the-art MIML algorithms.

Multi-Instance Multi-Label with Application to High Resolution Remote Sensing Images

In this paper, we seek to solve the problem of remote sensing image classification using Multi Instance Multi Label Learning (MIML) framework, where each image contain multiple regions (instances) corresponds to multiple objects (labels). MIML framework is more efficient than traditional learning framework for complicated objects such as satellite images. Existing MIML algorithms such as MIMLboost, MIMLsvm and MIMLfast have been found useful in scene classification and can provide multiple labels for each instance in complicated objects. The proposed approach was performed in two steps: In the first step, we process the dataset using segmentation and feature extraction. Images are ambiguous because they contain numerous objects so we consider that each image is a bag and various blocks in the image are instances. In the second step, we apply MIML to classify each block of images. According to the experimental results, the proposed method outperforms the state-of-the-art methods.

An improved multi-instance multi-label learning algorithm based on representative instances selection and label correlations

Multi-Instance Multi-Label Learning (MIML) has been successfully used in image and text classification problems. It is noteworthy that few of the previous studies consider the pattern-label relations. Inevitably, there are some useless instances in a bag which will reduce the accuracy of the annotation. In this paper we focus on this problem. Firstly, an instance selection method via joint l2,1-norms constraint is employed to eliminate the useless instances and select the representative instances by modelling the instance correlation. Then, bags are mapped to these representative instances. Finally, the classifier is trained by an optimisation algorithm based on label correlations. Experimental results on image data set, text data sets and bird song audio data set show that the proposed algorithm significantly improves the performance of MIML classifier compared with the state-of-the-art methods.

An improved multi-instance multi-label learning algorithm based on representative instances selection and label correlations

An improved multi-instance multi-label learning algorithm based on representative instances selection and label correlations

Multi-Instance Multi-Label Learning for Multi-Class Classification of Whole Slide Breast Histopathology Images.

Digital pathology has entered a new era with the availability of whole slide scanners that create the high-resolution images of full biopsy slides. Consequently, the uncertainty regarding the correspondence between the image areas and the diagnostic labels assigned by pathologists at the slide level, and the need for identifying regions that belong to multiple classes with different clinical significances have emerged as two new challenges. However, generalizability of the state-of-the-art algorithms, whose accuracies were reported on carefully selected regions of interest (ROIs) for the binary benign versus cancer classification, to these multi-class learning and localization problems is currently unknown. This paper presents our potential solutions to these challenges by exploiting the viewing records of pathologists and their slide-level annotations in weakly supervised learning scenarios. First, we extract candidate ROIs from the logs of pathologists' image screenings based on different behaviors, such as zooming, panning, and fixation. Then, we model each slide with a bag of instances represented by the candidate ROIs and a set of class labels extracted from the pathology forms. Finally, we use four different multi-instance multi-label learning algorithms for both slide-level and ROI-level predictions of diagnostic categories in whole slide breast histopathology images. Slide-level evaluation using 5-class and 14-class settings showed average precision values up to 81% and 69%, respectively, under different weakly labeled learning scenarios. ROI-level predictions showed that the classifier could successfully perform multi-class localization and classification within whole slide images that were selected to include the full range of challenging diagnostic categories.

A multi-instance multi-label learning algorithm based on instance correlations

Existing multi-instance multi-label learning algorithms generally assume that instances in a bag are independent of each other, which is difficult to be guaranteed in practical applications. A nove...

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

Multi-instance multi-label (MIML) learning has been proven to be effective for the genome-wide protein function prediction problems where each training example is associated with not only multiple instances but also multiple class labels. To find an appropriate MIML learning method for genome-wide protein function prediction, many studies in the literature attempted to optimize objective functions in which dissimilarity between instances is measured using the Euclidean distance. But in many real applications, Euclidean distance may be unable to capture the intrinsic similarity/dissimilarity in feature space and label space. Unlike other previous approaches, in this paper, we propose to learn a multi-instance multi-label distance metric learning framework (MIMLDML) for genome-wide protein function prediction. Specifically, we learn a Mahalanobis distance to preserve and utilize the intrinsic geometric information of both feature space and label space for MIML learning. In addition, we try to deal with the sparsely labeled data by giving weight to the labeled data. Extensive experiments on seven real-world organisms covering the biological three-domain system (i.e., archaea, bacteria, and eukaryote; Woese et al., 1990) show that the MIMLDML algorithm is superior to most state-of-the-art MIML learning algorithms.

Sparse Markov chain-based semi-supervised multi-instance multi-label method for protein function prediction.

Automated assignment of protein function has received considerable attention in recent years for genome-wide study. With the rapid accumulation of genome sequencing data produced by high-throughput experimental techniques, the process of manually predicting functional properties of proteins has become increasingly cumbersome. Such large genomics data sets can only be annotated computationally. However, automated assignment of functions to unknown protein is challenging due to its inherent difficulty and complexity. Previous studies have revealed that solving problems involving complicated objects with multiple semantic meanings using the multi-instance multi-label (MIML) framework is effective. For the protein function prediction problems, each protein object in nature may associate with distinct structural units (instances) and multiple functional properties (class labels) where each unit is described by an instance and each functional property is considered as a class label. Thus, it is convenient and natural to tackle the protein function prediction problem by using the MIML framework. In this paper, we propose a sparse Markov chain-based semi-supervised MIML method, called Sparse-Markov. A sparse transductive probability graph is constructed to encode the affinity information of the data based on ensemble of Hausdorff distance metrics. Our goal is to exploit the affinity between protein objects in the sparse transductive probability graph to seek a sparse steady state probability of the Markov chain model to do protein function prediction, such that two proteins are given similar functional labels if they are close to each other in terms of an ensemble Hausdorff distance in the graph. Experimental results on seven real-world organism data sets covering three biological domains show that our proposed Sparse-Markov method is able to achieve better performance than four state-of-the-art MIML learning algorithms.

Markov-Miml: A Markov chain-based multi-instance multi-label learning algorithm

The main aim of this paper is to propose an efficient and novel Markov chain-based multi-instance multi-label (Markov-Miml) learning algorithm to evaluate the importance of a set of labels associated with objects of multiple instances. The algorithm computes ranking of labels to indicate the importance of a set of labels to an object. Our approach is to exploit the relationships between instances and labels of objects. The rank of a class label to an object depends on (i) the affinity metric between the bag of instances of this object and the bag of instances of the other objects, and (ii) the rank of a class label of similar objects. An object, which contains a bag of instances that are highly similar to bags of instances of the other objects with a high rank of a particular class label, receives a high rank of this class label. Experimental results on benchmark data have shown that the proposed algorithm is computationally efficient and effective in label ranking for MIML data. In the comparison, we find that the classification performance of the Markov-Miml algorithm is competitive with those of the three popular MIML algorithms based on boosting, support vector machine, and regularization, but the computational time required by the proposed algorithm is less than those by the other three algorithms.

Learnability of multi-instance multi-label learning

Multi-instance multi-label learning (MIML) is a new machine learning framework where one data object is described by multiple instances and associated with multiple class labels. During the past few years, many MIML algorithms have been developed and many applications have been described. However, there lacks theoretical exploration to the learnability of MIML. In this paper, through proving a generalization bound for multi-instance single-label learner and viewing MIML as a number of multi-instance single-label learning subtasks with the correlation among the labels, we show that the MIML hypothesis class constructed from a multi-instance single-label hypothesis class is PAC-learnable.

Bayesian multi-instance multi-label learning using Gaussian process prior

Multi-instance multi-label learning (MIML) is a newly proposed framework, in which the multi-label problems are investigated by representing each sample with multiple feature vectors named instances. In this framework, the multi-label learning task becomes to learn a many-to-many relationship, and it also offers a possibility for explaining why a concerned sample has the certain class labels. The connections between instances and labels as well as the correlations among labels are equally crucial information for MIML. However, the existing MIML algorithms can rarely exploit them simultaneously. In this paper, a new MIML algorithm is proposed based on Gaussian process. The basic idea is to suppose a latent function with Gaussian process prior in the instance space for each label and infer the predictive probability of labels by integrating over uncertainties in these functions using the Bayesian approach, so that the connection between instances and every label can be exploited by defining a likelihood function and the correlations among labels can be identified by the covariance matrix of the latent functions. Moreover, since different relationships between instances and labels can be captured by defining different likelihood functions, the algorithm may be used to deal with the problems with various multi-instance assumptions. Experimental results on several benchmark data sets show that the proposed algorithm is valid and can achieve superior performance to the existing ones.

Transductive Multi-Instance Multi-Label learning algorithm with application to automatic image annotation

Automatic image annotation has emerged as an important research topic due to its potential application on both image understanding and web image search. Due to the inherent ambiguity of image-label mapping and the scarcity of training examples, the annotation task has become a challenge to systematically develop robust annotation models with better performance. From the perspective of machine learning, the annotation task fits both multi-instance and multi-label learning framework due to the fact that an image is usually described by multiple semantic labels (keywords) and these labels are often highly related to respective regions rather than the entire image. In this paper, we propose an improved Transductive Multi-Instance Multi-Label (TMIML) learning framework, which aims at taking full advantage of both labeled and unlabeled data to address the annotation problem. The experiments over the well known Corel 5000 data set demonstrate that the proposed method is beneficial in the image annotation task and outperforms most existing image annotation algorithms.