Extended Tree Augmented Naive Classifier

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds’ algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). A range of experiments show that we obtain models with better accuracy than TAN and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator.

A tree augmented classifier based on Extreme Imprecise Dirichlet Model

We present TANC, a TAN classifier (tree-augmented naive) based on imprecise probabilities. TANC models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM). A first contribution of this paper is the experimental comparison between EDM and the global Imprecise Dirichlet Model using the naive credal classifier (NCC), with the aim of showing that EDM is a sensible approximation of the global IDM. TANC is able to deal with missing data in a conservative manner by considering all possible completions (without assuming them to be missing-at-random), but avoiding an exponential increase of the computational time. By experiments on real data sets, we show that TANC is more reliable than the Bayesian TAN and that it provides better performance compared to previous TANs based on imprecise probabilities. Yet, TANC is sometimes outperformed by NCC because the learned TAN structures are too complex; this calls for novel algorithms for learning the TAN structures, better suited for an imprecise probability classifier.

A Tree Augmented Classifier Based on Extreme Imprecise Dirichlet Model

In this paper we present TANC, i.e., a tree-augmented naive credal classifier based on imprecise probabilities; it models prior near-ignorance via the Extreme Imprecise Dirichlet Model (EDM) (Cano et al., 2007) and deals conservatively with missing data in the training set, without assuming them to be missing-at-random. The EDM is an approximation of the global Imprecise Dirichlet Model (IDM), which considerably simplifies the computation of upper and lower probabilities; yet, having been only recently introduced, the quality of the provided approximation needs still to be verified. As first contribution, we extensively compare the output of the naive credal classifier (one of the few cases in which the global IDM can be exactly implemented) when learned with the EDM and the global IDM; the output of the classifier appears to be identical in the vast majority of cases, thus supporting the adoption of the EDM in real classification problems. Then, by experiments we show that TANC is more reliable than the precise TAN (learned with uniform prior), and also that it provides better performance compared to a previous (Zaffalon, 2003) TAN model based on imprecise probabilities. TANC treats missing data by considering all possible completions of the training set, but avoiding an exponential increase of the computational times; eventually, we present some preliminary results with missing data.

A cross-sectional study demonstrating increased serum amyloid A-related inflammation in high density lipoproteins from subjects with type 1 diabetes mellitus and how this association was augmented by poor glycaemic control

Inflammatory atherosclerosis is increased in subjects with type 1 diabetes mellitus (T1DM). Normally high-density lipoproteins(HDL) protect against atherosclerosis; however, in the presence of serum amyloid-A- (SAA-) related inflammation this propertymay be reduced. Fasting blood was obtained from fifty subjects with T1DM, together with fifty age, gender and BMI matchedcontrol subjects. HDL was subfractionated into HDL2 and HDL3 by rapid ultracentrifugation. Serum-hsCRP and serum-, HDL2-,and HDL3-SAA were measured by ELISAs. Compared to control subjects, SAA was increased in T1DM subjects, nonsignificantly inserum (P = 0.088), and significantly in HDL2 (P  = 0.003) and HDL3 (P  = 0.005). When the T1DM group were separated accordingto mean HbA1c (8.34%), serum-SAA and HDL3-SAA levels were higher in the T1DM subjects with HbA1c ≥ 8.34%, compared towhen HbA1c was <8.34% (P  < 0.05). Furthermore, regression analysis illustrated, that for every 1%-unit increase in HbA1c, SAAincreased by 20% and 23% in HDL2 and HDL3, respectively, independent of BMI. HsCRP did not differ between groups (P  > 0.05).This cross-sectional study demonstrated increased SAA-related inflammation in subjects with T1DM that was augmented by poorglycaemic control. We suggest that SAA is a useful inflammatory biomarker in T1DM, which may contribute to their increasedatherosclerosis risk.

Vertex Clustering of Augmented Graph Streams

In this paper we propose a graph stream clustering algorithm with a unied similarity measure on both structural and attribute properties of vertices, with each attribute being treated as a vertex. Unlike others, our approach does not require an input parameter for the number of clusters, instead, it dynamically creates new sketch-based clusters and periodically merges existing similar clusters. Experiments on two publicly available datasets reveal the advantages of our approach in detecting vertex clusters in the graph stream. We provide a detailed investigation into how parameters affect the algorithm performance. We also provide a quantitative evaluation and comparison with a well-known offline community detection algorithm which shows that our streaming algorithm can achieve comparable or better average cluster purity.

Sonification as Concurrent Augmented Feedback for Motor Skill Learning and the Importance of Mapping Design

In recent years, sonification of movement has emerged as a viable method for the provision of feedback in motor learning. Despite some experimental validation of its utility, controlled trials to test the usefulness of sonification in a motor learning context are still rare. As such, there are no accepted conventions for dealing with its implementation. This article addresses the question of how continuous movement information should be best presented as sound to be fed back to the learner. It is proposed that to establish effective approaches to using sonification in this context, consideration must be given to the processes that underlie motor learning, in particular the nature of the perceptual information available to the learner for performing the task at hand. Although sonification has much potential in movement performance enhancement, this potential is largely unrealised as of yet, in part due to the lack of a clear framework for sonification mapping: the relationship between movement and sound. By grounding mapping decisions in a firmer understanding of how perceptual information guides learning, and an embodied cognition stance in general, it is hoped that greater advances in use of sonification to enhance motor learning can be achieved.

Averaged Extended Tree Augmented Naive Classifier

This work presents a new general purpose classifier named Averaged Extended Tree Augmented Naive Bayes (AETAN), which is based on combining the advantageous characteristics of Extended Tree Augmented Naive Bayes (ETAN) and Averaged One-Dependence Estimator (AODE) classifiers. We describe the main properties of the approach and algorithms for learning it, along with an analysis of its computational time complexity. Empirical results with numerous data sets indicate that the new approach is superior to ETAN and AODE in terms of both zero-one classification accuracy and log loss. It also compares favourably against weighted AODE and hidden Naive Bayes. The learning phase of the new approach is slower than that of its competitors, while the time complexity for the testing phase is similar. Such characteristics suggest that the new classifier is ideal in scenarios where online learning is not required.

Learning Extended Tree Augmented Naive Structures

This work proposes an extended version of the well-known tree-augmented naive Bayes (TAN) classifier where the structure learning step is performed without requiring features to be connected to the class. Based on a modification of Edmonds' algorithm, our structure learning procedure explores a superset of the structures that are considered by TAN, yet achieves global optimality of the learning score function in a very efficient way (quadratic in the number of features, the same complexity as learning TANs). We enhance our procedure with a new score function that only takes into account arcs that are relevant to predict the class, as well as an optimization over the equivalent sample size during learning. These ideas may be useful for structure learning of Bayesian networks in general. A range of experiments shows that we obtain models with better prediction accuracy than naive Bayes and TAN, and comparable to the accuracy of the state-of-the-art classifier averaged one-dependence estimator (AODE). We release our implementation of ETAN so that it can be easily installed and run within Weka.

Generating Data Augmented Spectroscopic Data For Performance Enhancement

The application of chemometrics in food science has revolutionized the field by allowing the creation of models able to automate a broad range of applications such as food authenticity and food fraud detection. In order to create effective and general models able to address the complexity of real life problems, a vast amount of varied training samples are required. Training dataset has to cover all possible types of sample and instrument variability. However, acquiring a varied amount of samples is a time consuming and costly process, in which collecting samples representative of the real world variation is not always possible, specially in some application fields. To address this problem, a novel framework for the application of data augmentation techniques to spectroscopic data has been designed and implemented. This is a carefully designed pipeline of four complementary and independent blocks which can be finely tuned depending on the desired variance for enhancing model's robustness: a) blending spectra, b) changing baseline, c) shifting along x axis, and d) adding random noise.
This novel data augmentation solution has been tested in order to obtain highly efficient generalised classification model based on spectroscopic data. Fourier transform mid-infrared (FT-IR) spectroscopic data of eleven pure vegetable oils (106 admixtures) for the rapid identification of vegetable oil species in mixtures of oils have been used as a case study to demonstrate the influence of this pioneering approach in chemometrics, obtaining a 10% improvement in classification which is crucial in some applications of food adulteration.

Transposing Musical Skill: Sonification of movement as concurrent augmented feedback enhances learning in a bimanual task

Concurrent feedback provided during acquisition can enhance performance of novel tasks. The ‘guidance hypothesis’ predicts that feedback provision leads to dependence and poor performance in its absence. However, appropriately-structured feedback information provided through sound (‘sonification’) may not be subject to this effect. We test this directly using a rhythmic bimanual shape-tracing task in which participants learned to move at a 4:3 timing ratio. Sonification of movement and demonstration was compared to two other learning conditions: (1) sonification of task demonstration alone and (2) completely silent practice (control). Sonification of movement emerged as the most effective form of practice, reaching significantly lower error scores than control. Sonification of solely the demonstration, which was expected to benefit participants by perceptually unifying task requirements, did not lead to better performance than control. Good performance was maintained by participants in the sonification condition in an immediate retention test without feedback, indicating that the use of this feedback can overcome the guidance effect. On a 24-hour retention test, performance had declined and was equal between groups. We argue that this and similar findings in the feedback literature are best explained by an ecological approach to motor skill learning which places available perceptual information at the highest level of importance.

Fast Geodesic Active Fields for Image Registration Based on Splitting and Augmented Lagrangian Approaches.

In this paper, we present an efficient numerical scheme for the recently introduced geodesic active fields (GAF) framework for geometric image registration. This framework considers the registration task as a weighted minimal surface problem. Hence, the data-term and the regularization-term are combined through multiplication in a single, parametrization invariant and geometric cost functional. The multiplicative coupling provides an intrinsic, spatially varying and data-dependent tuning of the regularization strength, and the parametrization invariance allows working with images of nonflat geometry, generally defined on any smoothly parametrizable manifold. The resulting energy-minimizing flow, however, has poor numerical properties. Here, we provide an efficient numerical scheme that uses a splitting approach; data and regularity terms are optimized over two distinct deformation fields that are constrained to be equal via an augmented Lagrangian approach. Our approach is more flexible than standard Gaussian regularization, since one can interpolate freely between isotropic Gaussian and anisotropic TV-like smoothing. In this paper, we compare the geodesic active fields method with the popular Demons method and three more recent state-of-the-art algorithms: NL-optical flow, MRF image registration, and landmark-enhanced large displacement optical flow. Thus, we can show the advantages of the proposed FastGAF method. It compares favorably against Demons, both in terms of registration speed and quality. Over the range of example applications, it also consistently produces results not far from more dedicated state-of-the-art methods, illustrating the flexibility of the proposed framework.

Effects of Proactive and Retroactive Augmented Information on Physiological Responses in Learning a Novel Motor Skill

Previous research has demonstrated superior learning by participants presented with augmented task information retroactively versus proactively (Patterson & Lee, 2008; 2010). Theoretical explanations of these findings are related to the cognitive effort invested by participants during motor skill acquisition. The present study extended previous research by utilizing the physiological index, power spectral analysis of heart rate variability, previously shown to be sensitive to the degree of cognitive effort invested during the performance of a motor task (e.g., increase cognitive effort results in increased LF/HF ratio). Participants were required to learn 18 different key-pressing sequences. As expected, the proactive condition demonstrated superior RS during acquisition, with the retroactive condition demonstrating superior RS during retention. Measures of LF/HF ratio indicated the retroactive participants were investing significantly less cognitive effort in the retention period compared to the proactive participants (p< .05) as a function of learning.