Evolutionary structure learning algorithm for Bayesian network and penalized mutual information metric

This paper formulates the problem of learning Bayesian network structures from data as determining the structure that best approximates the probability distribution indicated by the data. A new metric, Penalized Mutual Information metric, is proposed, and a evolutionary algorithm is designed to search for the best structure among alternatives. The experimental results show that this approach is reliable and promising.

The "strugglers" in selected computer based mathematics classrooms

In this paper, the experiences of lower achieving mathematics students in two secondary mathematics classrooms in which computers were regularly used are described. A year 8 and a year 9 mathematics class from one secondary school participated in the ethnographic study. The results show that in these two classrooms the learning relationships and power relationships did not, in general, support the learning and engagement of lower achievers in mathematics. Research into computer based teaching methods that engage low achieving students in computer based mathematics is needed.

Discovery of activity structures using the hierarchical hidden Markov model

In building a surveillance system for monitoring people behaviours, it is important to understand the typical patterns of people's movement in the environment. This task is difficult when dealing with high-level behaviours. The flat model such as the hidden Markov model (HMM) is inefficient in differentiating between signatures of such behaviours. This paper examines structure learning for high-level behaviours using the hierarchical hidden Markov model (HHMM).We propose a two-phase learning algorithm in which the parameters of the behaviours at low levels are estimated first and then the structures and parameters of the behaviours at high levels are learned from multi-camera training data. Our algorithm is then evaluated using data from a real environment, demonstrating the robustness of the learned structure in recognising people's behaviour.

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.

Medical data classification using interval type-2 fuzzy logic system and wavelets

This paper introduces an automated medical data classification method using wavelet transformation (WT) and interval type-2 fuzzy logic system (IT2FLS). Wavelet coefficients, which serve as inputs to the IT2FLS, are a compact form of original data but they exhibits highly discriminative features. The integration between WT and IT2FLS aims to cope with both high-dimensional data challenge and uncertainty. IT2FLS utilizes a hybrid learning process comprising unsupervised structure learning by the fuzzy c-means (FCM) clustering and supervised parameter tuning by genetic algorithm. This learning process is computationally expensive, especially when employed with high-dimensional data. The application of WT therefore reduces computational burden and enhances performance of IT2FLS. Experiments are implemented with two frequently used medical datasets from the UCI Repository for machine learning: the Wisconsin breast cancer and Cleveland heart disease. A number of important metrics are computed to measure the performance of the classification. They consist of accuracy, sensitivity, specificity and area under the receiver operating characteristic curve. Results demonstrate a significant dominance of the wavelet-IT2FLS approach compared to other machine learning methods including probabilistic neural network, support vector machine, fuzzy ARTMAP, and adaptive neuro-fuzzy inference system. The proposed approach is thus useful as a decision support system for clinicians and practitioners in the medical practice. copy; 2015 Elsevier B.V. All rights reserved.

Mass spectrometry-based proteomic data for cancer diagnosis using interval type-2 fuzzy system

An interval type-2 fuzzy logic system is introduced for cancer diagnosis using mass spectrometry-based proteomic data. The fuzzy system is incorporated with a feature extraction procedure that combines wavelet transform and Wilcoxon ranking test. The proposed feature extraction generates feature sets that serve as inputs to the type-2 fuzzy classifier. Uncertainty, noise and outliers that are common in the proteomic data motivate the use of type-2 fuzzy system. Tabu search is applied for structure learning of the fuzzy classifier. Experiments are performed using two benchmark proteomic datasets for the prediction of ovarian and pancreatic cancer. The dominance of the suggested feature extraction as well as type-2 fuzzy classifier against their competing methods is showcased through experimental results. The proposed approach therefore is helpful to clinicians and practitioners as it can be implemented as a medical decision support system in practice.

Collaborative filtering via sparse Markov random fields

Recommender systems play a central role in providing individualized access to information and services. This paper focuses on collaborative filtering, an approach that exploits the shared structure among mind-liked users and similar items. In particular, we focus on a formal probabilistic framework known as Markov random fields (MRF). We address the open problem of structure learning and introduce a sparsity-inducing algorithm to automatically estimate the interaction structures between users and between items. Item-item and user-user correlation networks are obtained as a by-product. Large-scale experiments on movie recommendation and date matching datasets demonstrate the power of the proposed method.

Understanding and developing structure - its importance for mathematics learning

This issue comes at a time when mathematics education research is becoming more intently focused on the development of "structure" as salient to generalised mathematics learning. Not surprisingly the attention on structure creates particular synergies with the increasingly rich field of research on algebraic thinking and arithmetic processes, particularly in the early years. In many ways, this special issue is concerned with describing the process of "structuring" that enables abstraction and generalisation. A recent MERJ special issue, Abstraction in Mathematics Education (Mitchelmore & White, 2007), illustrated theories of abstraction aligning these to notions of underlying structure. The importance of structure in the transition from school to university was also highlighted by Godfrey and Thomas (2008), and Novotna and Hoch (2008) in the previous special issue of MERJ (Thomas, 2008). In this special issue we present six papers that provide evidence of developing structure as critical for all learners of mathematics throughout schooling.

Tracking-as-recognition for articulated full-body human motion analysis

This paper addresses the problem of markerless tracking of a human in full 3D with a high-dimensional (29D) body model Most work in this area has been focused on achieving accurate tracking in order to replace marker-based motion capture, but do so at the cost of relying on relatively clean observing conditions. This paper takes a different perspective, proposing a body-tracking model that is explicitly designed to handle real-world conditions such as occlusions by scene objects, failure recovery, long-term tracking, auto-initialisation, generalisation to different people and integration with action recognition. To achieve these goals, an action's motions are modelled with a variant of the hierarchical hidden Markov model The model is quantitatively evaluated with several tests, including comparison to the annealed particle filter, tracking different people and tracking with a reduced resolution and frame rate.

Automatically learning structural units in educational videos with the hierarchical hidden Markov models

In this paper we present a coherent approach using the hierarchical HMM with shared structures to extract the structural units that form the building blocks of an education/training video. Rather than using hand-crafted approaches to define the structural units, we use the data from nine training videos to learn the parameters of the HHMM, and thus naturally extract the hierarchy. We then study this hierarchy and examine the nature of the structure at different levels of abstraction. Since the observable is continuous, we also show how to extend the parameter learning in the HHMM to deal with continuous observations.

Narrative structure analysis with education and training videos for e-learning

This paper deals with the problem ofstructuralizing education and training videos for high-level semantics extraction and nonlinear media presentation in e-learning applications. Drawing guidance from production knowledge in instructional media, we propose six main narrative structures employed in education and training videos for both motivation and demonstration during learning and practical training. We devise a powerful audiovisual feature set, accompanied by a hierarchical decision tree-based classification system to determine and discriminate between these structures. Based on a two-liered hierarchical model, we demonstrate that we can achieve an accuracy of 84.7% on a comprehensive set of education and training video data.

Indexing narrative structure and semantics in motion pictures with a probabilistic framework

This work constitutes the first attempt to extract an important narrative structure, the 3-Act story telling paradigm, in film. This narrative structure is prevalent in the domain of film as it forms the foundation and framework in which the film can be made to function as an effective tool for story telling, and its extraction is a vital step in automatic content management for film data. A novel act boundary likelihood function for Act 1 is derived using a Bayesian formulation under guidance from film grammar, tested under many configurations and the results are reported for experiments involving 25 full length movies. The formulation is shown to be a useful tool in both the automatic and semi-interactive setting for semantic analysis of film.

Classifying and learning cricket shots using camera motion

This paper presents a method to classify and learn cricket shots. The procedure begins by extracting the camera motion parameters from the shots. Then the camera parameter values are converted to symbolic form and combined to generate a symbolic description that defines the trajectory of the cricket bell. The description generated is used to classify the cricket shot and to dynamically expand or update the system's knowledge of shots. The first novel aspect of this approach is that by using the camera motion parameters, a complex and difficult process of low level image segmenting of either the batsman or the cricket ball from video images is avoided. Also the method does not require high resolution images. Another novel aspect of this work is the use of a new incremental learning algorithm that enables the system to improve and update its knowledge base. Unlike previously developed algorithms which store training instances and have simple method to prime their concept hierarchies, the incremental learning algorithm used in this work generates compact concept hierarchies and uses evidence based forgetting. The results show that the system performs well in the task of classifying four types of cricket shots.