Fuzzy rule-based systems for recognition-intensive classification in granular computing context

In traditional machine learning, classification is typically undertaken in the way of discriminative learning using probabilistic approaches, i.e. learning a classifier that discriminates one class from other classes. The above learning strategy is mainly due to the assumption that different classes are mutually exclusive and each instance is clear-cut. However, the above assumption does not always hold in the context of real-life data classification, especially when the nature of a classification task is to recognize patterns of specific classes. For example, in the context of emotion detection, multiple emotions may be identified from the same person at the same time, which indicates in general that different emotions may involve specific relationships rather than mutual exclusion. In this paper, we focus on classification problems that involve pattern recognition. In particular, we position the study in the context of granular computing, and propose the use of fuzzy rule-based systems for recognition-intensive classification of real-life data instances. Furthermore, we report an experimental study conducted using 7 UCI data sets on life sciences, to compare the fuzzy approach with four popular probabilistic approaches in pattern recognition tasks. The experimental results show that the fuzzy approach can not only be used as an alternative one to the probabilistic approaches but also is capable to capture more patterns which probabilistic approaches cannot achieve

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

Gender discriminating models from facial surface normals

In this paper, we show how to use facial shape information to construct discriminating models for gender classification. We represent facial shapes using 2.5D fields of facial surface normals, and investigate three different methods to improve the gender discriminating capacity of the model constructed using the standard eigenspace method. The three methods are novel variants of principal geodesic analysis (PGA) namely (a) weighted PGA, (b) supervised weighted PGA, and (c) supervised PGA. Our starting point is to define a weight map over the facial surface that indicates the importance of different locations in discriminating gender. We show how to compute the relevant weights and how to incorporate the weights into the 2.5D model construction. We evaluate the performance of the alternative methods using facial surface normals extracted from 3D range images or recovered from brightness images. Experimental results demonstrate the effectiveness of our methods. Moreover, the classification accuracy, which is as high as 97%, demonstrates the effectiveness of using facial shape information for gender classification

Characterization of normal facial features and their association with genes

ABSTRACT Background: Craniofacial morphology has been reported to be highly heritable, but little is known about which genetic variants influence normal facial variation in the general population. Aim: To identify facial variation and explore phenotype-genotype associations in a 15-year-old population (2514 females and 2233 males). Subjects and Methods: The subjects involved in this study were recruited from the Avon Longitudinal Study of Parents and Children (ALSPAC). Three-dimensional (3D) facial images were obtained for each subject using two high-resolution Konica Minolta laser scanners. Twenty-one reproducible facial soft tissue landmarks and one constructed mid-endocanthion point (men) were identified and their coordinates were recorded. The 3D facial images were registered using Procrustes analysis (with and without scaling). Principal Component Analysis (PCA) was then employed to identify independent groups ‘principal components, PCs’ of correlated landmark coordinates that represent key facial features contributing to normal facial variation. A novel surface-based method of facial averaging was employed to visualize facial variation. Facial parameters (distances, angles, and ratios) were also generated using facial landmarks. Sex prediction based on facial parameters was explored using discriminant function analysis. A discovery-phase genome-wide association analysis (GWAS) was carried out for 2,185 ALSPAC subjects and replication was undertaken in a further 1,622 ALSPAC individuals. Results: 14 (unscaled) and 17 (scaled) PCs were identified explaining 82% of the total variance in facial form and shape. 250 facial parameters were derived (90 distances, 118 angles, 42 ratios). 24 facial parameters were found to provide sex prediction efficiency of over 70%, 23 of these parameters are distances that describe variation in face height, nose width, and prominence of various facial structures. 54 distances associated with previous reported high heritability and the 14 (unscaled) PCs were included in the discovery-phase GWAS. Four genetic associations with the distances were identified in the discovery analysis, and one of these, the association between the common ‘intronic’ SNP (rs7559271) in PAX3 gene on chromosome (2) and the nasion to mid-endocanthion 3D distance (n-men) was replicated strongly (p = 4 x 10-7). PAX3 gene encodes a transcription factor that plays crucial role in fetal development including craniofacial bones. PAX3 contains two DNA-binding domains, a paired-box domain and a homeodomain. The protein made from PAX3 gene directs the activity of other genes that signal neural crest cells to form specialized tissues such as craniofacial bones. PAX3 different mutations may lead to non-functional PAX3 polypeptides and destroy the ability of the PAX3 proteins to bind to DNA and regulate the activity of other genes to form bones and other specific tissues. Conclusions: The variation in facial form and shape can be accurately quantified and visualized as a multidimensional statistical continuum with respect to the principal components. The derived PCs may be useful to identify and classify faces according to a scale of normality. A strong genetic association was identified between the common SNP (rs7559271) in PAX3 gene on chromosome (2) and the nasion to mid-endocanthion 3D distance (n-men). Variation in this distance leads to nasal bridge prominence