Changjing Shang

Evolutionary Fuzzy Extreme Learning Machine for Mammographic Risk Analysis

Mammographic risk analysis is an important and challenging issue in modern medical science; research and development in this area has recently attracted much attention. Many efforts have been devoted to achieving a higher accuracy in such analysis.This paper presents a novel approach for automated analysis of mammographic risk, in support of human consultant estimation of such risk. The underlying approach is general, it combines evolutionary computation with extreme learning machine to efficiently train effective fuzzy systems. The proposed approach is experimentally compared to a number of state-of-the-art learning classifiers that can also be adopted to analyze mammographic risk. The significance of this work is highlighted by its improved performance over the alternative approaches, measured using criteria such as classification accuracy and confusion matrices. The results demonstrate that for the problem of mammographic risk analysis, evolutionary fuzzy extreme learning machine entails such performance both at the overall image level and at the level of individual risk types.

Extending Data Reliability Measure to a Filter Approach for Soft Subspace Clustering

The measure of data reliability has recently proven useful for a number of data analysis tasks. This paper extends the underlying metric to a new problem of soft subspace clustering. The concept of subspace clustering has been increasingly recognized as an effective alternative to conventional algorithms (which search for clusters without differentiating the significance of different data attributes). While a large number of crisp subspace approaches have been proposed, only a handful of soft counterparts are developed with the common goal of acquiring the optimal cluster-specific dimension weights. Most soft subspace clustering methods work based on the exploitation of k-means and greatly rely on the iteratively disclosed cluster centers for the determination of local weights. Unlike such wrapper techniques, this paper presents a filter approach which is efficient and generally applicable to different types of clustering. Systematical experimental evaluations have been carried out over a collection of published gene expression data sets. The results demonstrate that the reliability-based methods generally enhance their corresponding baseline models and outperform several well-known subspace clustering algorithms.

Fuzzy-rough feature selection aided support vector machines for Mars image classification

This paper presents a novel application of advanced machine learning techniques for Mars terrain image classification. Fuzzy-rough feature selection (FRFS) is adapted and then employed in conjunction with Support Vector Machines (SVMs) to construct image classifiers. These techniques are integrated to address problems in space engineering where the images are of many classes, large-scale, and diverse representational properties. The use of the adapted FRFS allows the induction of low-dimensionality feature sets from feature patterns of a much higher dimensionality. To evaluate the proposed work, K-Nearest Neighbours (KNNs) and decision trees (DTREEs) based image classifiers as well as information gain rank (IGR) based feature selection are also investigated here, as possible alternatives to the underlying machine learning techniques adopted. The results of systematic comparative studies demonstrate that in general, feature selection improves the performance of classifiers that are intended for use in high dimensional domains. In particular, the proposed approach helps to increase the classification accuracy, while enhancing classification efficiency by requiring considerably less features. This is evident in that the resultant SVM-based classifiers which utilise FRFS-selected features generally outperform KNN and DTREE based classifiers and those which use IGR-returned features. The work is therefore shown to be of great potential for on-board or ground-based image classification in future Mars rover missions.

A hierarchical fuzzy cluster ensemble approach and its application to big data clustering

This is the author accepted manuscript. The final version is available from IOS Press via http://dx.doi.org/10.3233/IFS-141518

A developmental approach to robotic pointing via human-robot interaction

Abstract The ability of pointing is recognised as an essential skill of a robot in its communication and social interaction. This paper introduces a developmental learning approach to robotic pointing, by exploiting the interactions between a human and a robot. The approach is inspired through observing the process of human infant development. It works by first applying a reinforcement learning algorithm to guide the robot to create attempt movements towards a salient object that is out of the robot’s initial reachable space. Through such movements, a human demonstrator is able to understand the robot desires to touch the target and consequently, to assist the robot to eventually reach the object successfully. The human–robot interaction helps establish the understanding of pointing gestures in the perception of both the human and the robot. From this, the robot can collect the successful pointing gestures in an effort to learn how to interact with humans. Developmental constraints are utilised to drive the entire learning procedure. The work is supported by experimental evaluation, demonstrating that the proposed approach can lead the robot to gradually gain the desirable pointing ability. It also allows that the resulting robot system exhibits similar developmental progress and features as with human infants.

Kernel-based fuzzy-rough nearest neighbour classification

Fuzzy-rough sets play an important role in dealing with imprecision and uncertainty for discrete and real-valued or noisy data. However, there are some problems associated with the approach from both theoretical and practical viewpoints. These problems have motivated the hybridisation of fuzzy-rough sets with kernel methods. Existing work which hybridises fuzzy-rough sets and kernel methods employs a constraint that enforces the transitivity of the fuzzy T-norm operation. In this paper, such a constraint is relaxed and a new kernel-based fuzzy-rough set approach is introduced. Based on this, novel kernel-based fuzzy-rough nearest-neighbour algorithms are proposed. The work is supported by experimental evaluation, which shows that the new kernel-based methods offer improvements over the existing fuzzy-rough nearest neighbour classifiers. The abstract goes here.

Facilitating efficient Mars terrain image classification with fuzzy-rough feature selection

This paper presents an application study of exploiting fuzzy-rough feature selection (FRFS) techniques in aid of efficient and accurate Mars terrain image classification. The employment of FRFS allows the induction of low-dimensionality feature sets from sample descriptions of feature vectors of a much higher dimensionality. Supported with comparative studies, the work demonstrates that FRFS helps to enhance both the effectiveness and the efficiency of conventional classification systems such as multi-layer perceptrons and K-nearest neighbors, by minimizing redundant and noisy features. This is of particular significance for on-board image classification in future Mars rover missions.

Link-based approach for bibliometric journal ranking

The ISI impact factor is widely accepted as a possible measurement of academic journal quality. However, much debate has recently surrounded this use, and several complex alternative journal impact indicators have been reported. To avoid the bias which may be caused by using a single quality indicator, ensemble of multiple indicators is a promising method for producing a more robust quality estimation. In this paper, an approach based on links between journals is proposed for the capturing and fusion of impact indicators. In particular, a number of popular indicators are combined and transformed to fused-links between academic journals, and two distance metrics: Euclidean distance and Manhattan distance are utilised to support the development and analysis of the fused-links. The approach is applied to both supervised and unsupervised learning, in an effort to estimate the impact and therefore the ranking of journals. Results of systematic experimental evaluation demonstrate that by exploiting the fused-links, simple algorithms such as K-Nearest Neighbours and K-means can perform as well as advanced techniques like support vector machines, in terms of accuracy and within-1 accuracy, while exhibiting the advantage of being more intuitive and interpretable.

Support vector machine-based classification of rock texture images aided by efficient feature selection

This paper presents a study on rock texture image classification using support vector machines (and also K-nearest neighbours and decision trees) with the aid of feature selection techniques. It offers both unsupervised and supervised methods for feature selection, based on data reliability and information gain ranking respectively. Following this approach, the conventional classifiers which are sensitive to the dimensionality of feature patterns, become effective on classification of images whose pattern representation may otherwise involve a large number of features. The work is successfully applied to complex images. Classifiers built using features selected by either of these methods generally outperform their counterparts that employ the full set of original features which has a dimensionality several folds higher than that of the selected feature subset. This is confirmed by systematic experimental investigations. This study therefore, helps to accomplish challenging image classification tasks effectively and efficiently. In particular, the approach retains the underlying semantics of a selected feature subset. This is very important to ensure that the classification results are understandable by the user.

Fuzzy similarity-based nearest-neighbour classification as alternatives to their fuzzy-rough parallels

Fuzzy-rough sets have enjoyed much attention in recent years as an effective way in which to extend rough set theory such that it can deal with real-valued data. More recently, fuzzy-rough sets have been employed for the task of classification. This has led to the development of approaches such as fuzzy-rough nearest-neighbour (FRNN) and its extension based on vaguely-quantified rough sets (VQNN). These methods perform well and experimental evaluation demonstrates that VQNN in particular is very effective for dealing with data in the presence of noise. In this paper, the underlying mechanisms of FRNN and VQNN are investigated and analysed. The theoretical proof and empirical evaluation show that the resulting classification of FRNN and VQNN depends only upon the highest similarity and greatest summation of the similarities of each class, respectively. This fact is exploited in order to formulate the novel methods proposed in this paper: similarity nearest-neighbour (SNN) and aggregated-similarity nearest-neighbour (ASNN). These two novel approaches are equivalent to FRNN and VQNN, but do not employ the concepts or framework of fuzzy-rough sets. Instead only fuzzy similarity is used. Experimental evaluation confirms the observation that these new methods maintain the classification performance of the existing advanced fuzzy-rough nearest-neighbour-based classifiers. In addition, the underlying mathematical foundation is simplified.