Multi-task learning for intelligent data processing in granular computing context

Classification is a popular task in many application areas, such as decision making, rating, sentiment analysis and pattern recognition. In the recent years, due to the vast and rapid increase in the size of data, classification has been mainly undertaken in the way of supervised machine learning. In this context, a classification task involves data labelling, feature extraction,feature selection and learning of classifiers. In traditional machine learning, data is usually single-labelled by experts, i.e., each instance is only assigned one class label, since experts assume that different classes are mutually exclusive and each instance is clear-cut. However, the above assumption does not always hold in real applications. For example, in the context of emotion detection, there could be more than one emotion identified from the same person. On the other hand, feature selection has typically been done by evaluating feature subsets in terms of their relevance to all the classes. However, it is possible that a feature is only relevant to one class, but is irrelevant to all the other classes. Based on the above argumentation on data labelling and feature selection, we propose in this paper a framework of multi-task learning. In particular, we consider traditional machine learning to be single task learning, and argue the necessity to turn it into multi-task learning to allow an instance to belong to more than one class (i.e., multi-task classification) and to achieve class specific feature selection (i.e.,multi-task feature selection). Moreover, we report two experimental studies in terms of fuzzy multi-task classification and rule learning based multi-task feature selection. The results show empirically that it is necessary to undertake multi-task learning for both classification and feature selection

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

Examining Swarm Intelligence-based Feature Selection for Multi-Label Classification

Multi-label classification addresses the issues that more than one class label assigns to each instance. Many real-world multi-label classification tasks are high-dimensional due to digital technologies, leading to reduced performance of traditional multi-label classifiers. Feature selection is a common and successful approach to tackling this problem by retaining relevant features and eliminating redundant ones to reduce dimensionality. There is several feature selection that is successfully applied in multi-label learning. Most of those features are wrapper methods that employ a multi-label classifier in their processes. They run a classifier in each step, which requires a high computational cost, and thus they suffer from scalability issues. To deal with this issue, filter methods are introduced to evaluate the feature subsets using information-theoretic mechanisms instead of running classifiers. This paper aims to provide a comprehensive review of different methods of feature selection presented for the tasks of multi-label classification. To this end, in this review, we have investigated most of the well-known and state-of-the-art methods. We then provided the main characteristics of the existing multi-label feature selection techniques and compared them analytically

Redefining Selection of Features and Classification Algorithms for Room Occupancy Detection

The exponential growth of todays technologies has resulted in the growth of high-throughput data with respect to both dimensionality and sample size. Therefore, efficient and effective supervision of these data becomes increasing challenging and machine learning techniques were developed with regards to knowledge discovery and recognizing patterns from these data. This paper presents machine learning tool for preprocessing tasks and a comparative study of different classification techniques in which a machine learning tasks have been employed in an experimental set up using a dataset archived from the UCI Machine Learning Repository website. The objective of this paper is to analyse the impact of refined feature selection on different classification algorithms to improve the prediction of classification accuracy for room occupancy. Subsets of the original features constructed by filter or information gain and wrapper techniques are compared in terms of the classification performance achieved with selected machine learning algorithms. Three feature selection algorithms are tested, specifically the Information Gain Attribute Evaluation (IGAE), Correlation Attribute Evaluation (CAE) and Wrapper Subset Evaluation (WSE) algorithms. Following a refined feature selection stage, three machine learning algorithms are then compared, consisting the Multi-Layer Perceptron (MLP), Logistic Model Trees (LMT) and Instance Based k (IBk). Based on the feature analysis, the WSE was found to be optimal in identifying relevant features. The application of feature selection is certainly intended to obtain a higher accuracy performance. The experimental results also demonstrate the effectiveness of Instance Based k compared to other ML classifiers in providing the highest performance rate of room occupancy prediction

Multi-label learning by extended multi-tier stacked ensemble method with label correlated feature subset augmentation

Classification is one of the basic and most important operations that can be used in data science and machine learning applications. Multi-label classification is an extension of the multi-class problem where a set of class labels are associated with a particular instance at a time. In a multiclass problem, a single class label is associated with an instance at a time. However, there are many different stacked ensemble methods that have been proposed and because of the complexity associated with the multi-label problems, there is still a lot of scope for improving the prediction accuracy. In this paper, we are proposing the novel extended multi-tier stacked ensemble (EMSTE) method with label correlationby feature subset selection technique and then augmenting those feature subsets while constructing the intermediate dataset for improving the prediction accuracy in the generalization phase of the stacking. The performance effect of the proposed method has been compared with existing methods and showed that our proposed method outperforms the other methods

A triple-random ensemble classification method for mining multi-label data

This paper presents a triple-random ensemble learning method for handling multi-label classification problems. The proposed method integrates and develops the concepts of random subspace, bagging and random k-label sets ensemble learning methods to form an approach to classify multi-label data. It applies the random subspace method to feature space, label space as well as instance space. The devised subsets selection procedure is executed iteratively. Each multi-label classifier is trained using the randomly selected subsets. At the end of the iteration, optimal parameters are selected and the ensemble MLC classifiers are constructed. The proposed method is implemented and its performance compared against that of popular multi-label classification methods. The experimental results reveal that the proposed method outperforms the examined counterparts in most occasions when tested on six small to larger multi-label datasets from different domains. This demonstrates that the developed method possesses general applicability for various multi-label classification problems.<br /

Multi-task ensemble creation for advancing performance of image segmentation

Image classification is a special type of applied machine learning tasks, where each image can be treated as an instance if there is only one target object that belongs to a specific class and needs to be recognized from an image. In the case of recognizing multiple target objects from an image, the image classification task can be formulated as image segmentation, leading to multiple instances being extracted from an image. In the setting of machine learning, each instance newly extracted from an image belongs to a specific class (a special type of target objects to be recognized) and presents specific features. In this context, in order to achieve effective recognition of each target object, it is crucial to undertake effective selection of features relevant to each specific class and appropriate setting of the training of classifiers on the selected features. In this paper, a multi-task approach of ensemble creation is proposed. The proposed approach is designed to first adopt multiple methods of multi-task feature selection for obtaining multiple groups of feature subsets (i.e., multiple subsets of features selected for each class), then to employ the KNN algorithm to create an ensemble of classifiers using each group of feature subsets resulting from a specific one of the multi-task feature selection methods, and finally all the ensembles are fused to classify each instance. We compare the performance obtained using our proposed way of ensemble creation with the one obtained using a single classifier trained on either a full set of original features or a reduced set of features selected using a single method of feature selection. The experimental results show some advances achieved in the image segmentation performance through using our proposed ensemble creation approaches, in comparison with the use of existing methods

Multi-task feature selection for advancing performance of image segmentation

Image segmentation is a popular application area of machine learning. In this context, each target region drawn from an image is defined as a class towards recognition of instances that belong to this region (class). In order to train classifiers that recognize the target region to which an instance belongs, it is important to extract and select features relevant to the region. In traditional machine learning, all features extracted from different regions are simply used together to form a single feature set for training classifiers, and feature selection is usually designed to evaluate the capability of each feature or feature subset in discriminating one class from other classes. However, it is possible that some features are only relevant to one class but irrelevant to all the other classes. From this point of view, it is necessary to undertake feature selection for each specific class, i.e, a relevant feature subset is selected for each specific class. In this paper, we propose the so-called multi-task feature selection approach for identifying features relevant to each target region towards effective image segmentation. This way of feature selection requires to transform a multi-class classification task into $n$ binary classification tasks, where $n$ is the number of classes. In particular, the Prism algorithm is used to produce a set of rules for class specific feature selection and the K nearest neighbour algorithm is used for training a classifier on a feature subset selected for each class. The experimental results show that the multi-task feature selection approach leads to an significant improvement of classification performance comparing with traditional feature selection approaches