A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author

Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin

Machine Learning for Hand Gesture Classification from Surface Electromyography Signals

Classifying hand gestures from Surface Electromyography (sEMG) is a process which has applications in human-machine interaction, rehabilitation and prosthetic control. Reduction in the cost and increase in the availability of necessary hardware over recent years has made sEMG a more viable solution for hand gesture classification. The research challenge is the development of processes to robustly and accurately predict the current gesture based on incoming sEMG data. This thesis presents a set of methods, techniques and designs that improve upon evaluation of, and performance on, the classification problem as a whole. These are brought together to set a new baseline for the potential classification. Evaluation is improved by careful choice of metrics and design of cross-validation techniques that account for data bias caused by common experimental techniques. A landmark study is re-evaluated with these improved techniques, and it is shown that data augmentation can be used to significantly improve upon the performance using conventional classification methods. A novel neural network architecture and supporting improvements are presented that further improve performance and is refined such that the network can achieve similar performance with many fewer parameters than competing designs. Supporting techniques such as subject adaptation and smoothing algorithms are then explored to improve overall performance and also provide more nuanced trade-offs with various aspects of performance, such as incurred latency and prediction smoothness. A new study is presented which compares the performance potential of medical grade electrodes and a low-cost commercial alternative showing that for a modest-sized gesture set, they can compete. The data is also used to explore data labelling in experimental design and to evaluate the numerous aspects of performance that must be traded off

Motive-Directed Meter

This dissertation isolates, defines, and explores the phenomenon of Motive-Directed Meter (MDM), which has hitherto received little scholarly attention. MDM is a listening experience evoked by music that is temporally regular enough to encourage metric listening and prediction, but irregular enough to frustrate these behaviors. MDM arises when recurring musical motives suggest parallel metric hearings, but shifting durational spans make metrical parallelism difficult to achieve. Listeners are therefore caught in a state of expectational limbo, urged to continually revise predictions that are recurrently thwarted. To approach this phenomenon, Chapter 1 describes the model of musical meter that undergirds this project, in which meter is viewed as an experiential process of temporal orientation taking place in the mind and body of a listener. Central to this dissertation is the notion that, like temporal orientation itself, the category “metric music” is not binary but graded, permitting degrees of inclusion; this removes the need to determine whether MDM can be considered “metric.” In order to accommodate this fluid conception, a flexible model of meter is introduced, which assesses the entrained listening experience according to four continua: timepoint specificity, pulse periodicity, hierarchic depth, and motivic saturation. These criteria are combined to create the multidimensional Flexible Metric Space, which accommodates all metric experiences, including Motive-Directed Meter, traditionally deep meter, and any other listening experience arising from synchronization with felt pulsation. This graded approach to membership in “metric music” allows analysts to compare and contrast musics from diverse repertoires. After Chapter 1 defines Motive-Directed Meter and the model of meter in which it is situated, Chapter 2 introduces five analytic tools appropriate to MDM. Some of these are adapted, some are newly developed, and each captures a different aspect of real-time listening. First, motive maps provide visual representations that summarize and highlight relationships between motives and durational spans, providing an overview of the interplay between these domains. Second, the variability index ranks categories of meter according to entrainment difficulty in isolation. Taken together, these two methods provide a rough picture of the shifting levels of unpredictability across a given passage of MDM. Third, Mark Gotham’s metric relations describe the relative difficulty and quality of connections between adjacent meters, further refining the processual approach undertaken here. Fourth, the metric displacement technique assesses the degree of mismatch between a listener’s expectations and realized musical events, comparing the expected metric depth—roughly, the metric strength—of certain important musical events with the “actual,” realized metric depth of those moments. This technique thereby describes the magnitude of the entrainment shift a listener must undertake in order to adjust to musical events at unexpected temporal positions. Fifth and finally, three expectation-generation methods are used to produce hypothetical sets of predictions intended to roughly approximate listener expectations at various stages of the learning process; these are local inertia, motivic inertia, and prototype methods. The utility of these analytic techniques is highlighted by way of a diverse series of analyses. Chapters 2 and 3 focus on the music of Igor Stravinsky: Chapter 2 analyzes brief passages from the Rite of Spring, the Soldier’s Tale, and Petrushka, while Chapter 3 delves deeply into three large works: the “Sacrificial Dance” and “Glorification of the Chosen One” from the Rite of Spring, and the “Feast at the Emperor’s Palace” from the Song of the Nightingale. Chapter 4 then moves beyond Stravinsky to explore the music of a large number of late twentieth- and early twenty-first century composers and popular music artists working in diverse styles and genres. The artists studied in this chapter include the composers Meredith Monk and Julia Wolfe, and the groups Rolo Tomassi and Mayors of Miyazaki. The analyses comprising this dissertation employ an experiential perspective, combining the techniques outlined above in order to better understand how we as listeners may work to orient ourselves to these pieces of music. In contrast to traditional structuralist approaches, all of the analyses presented in chapters 2-4, as well as the tools supporting them, are directed at the listening experience. Indeed, this dissertation—from its conceptions about meter and the tools it introduces, to the analyses that stem from both—is driven by a belief that the experience of the listener must lie at the heart of the analytic process. Central to all of the analyses is thus this aim: to illustrate how Motive-Directed Meter arises and to elucidate what it feels like to listen to it. With hope, this experience-driven approach may serve as a starting point for others seeking to similarly represent musical meter

Evaluation bias in effort estimation

There exists a large number of software effort estimation methods in the literature and the space of possibilities [54] is yet to be fully explored. There is little conclusive evidence about the relative performance of such methods and many studies suffer from instability in their conclusions. As a result, the effort estimation literature lacks a stable ranking of such methods.;This research aims at providing a stable ranking of a large number of methods using data sets based on COCOMO features. For this task, the COSEEKMO tool [46] was further developed into a benchmarking tool and several well-known effort estimation methods, including model trees, linear regression methods, local calibration, and several newly developed methods were used in COSEEKMO for a thorough comparison. The problem of instability was further explored and the evaluation method used was identified as the cause of instability. Therefore, the existing evaluation bias was corrected through a new evaluation approach, which was non-parametric. The Mann-Whitney U test [42] is the non-parametric test used in this study, which introduced a great amount of stability in the results. Several evaluation criteria were tested in order to analyze their possible effects on the observed stability.;The conclusions made in this study were stable across different evaluation criteria, different data sets, and different random runs. As a result, a group of four methods were selected as the best effort estimation methods among the explored 312 combinations of methods. These four methods were all based on the local calibration procedure proposed by Boehm [4]. Furthermore, these methods were simpler and more effective than many other complex methods including the Wrapper [37] and model trees [60], which are well-known methods in the literature.;Therefore, while there exists no single universal best method for effort estimation, this study suggests applying the four methods reported here to the historical data and using the best performing method among these four to estimate the effort for future projects. In addition, this study provides a path for comparing other existing or new effort estimation methods with the currently explored methods. This path involves a systematic comparison of the performance of each method against all other methods, including the methods studied in this work, through a benchmarking tool such as COSEEKMO, and using the non-parametric Mann-Whitney U test

Factors 2 and 3: Towards a principled approach

This paper seeks to make progress in our understanding of the non-UG components of Chomsky's (2005) Three Factors model. In relation to the input (Factor 2), I argue for the need to formulate a suitably precise hypothesis about which aspects of the input will qualify as 'intake' and, hence, serve as the basis for grammar construction. In relation to Factor 3, I highlight a specific cognitive bias that appears well motivated outside of language, while also having wide-ranging consequences for our understanding of how I-language grammars are constructed, and why they should have the crosslinguistically comparable form that generativists have always argued human languages have. This is Maximise Minimal Means (MMM). I demonstrate how its incorporation into our model of grammar acquisition facilitates understanding of diverse facts about natural language typology, acquisition, both in "stable" and "unstable" contexts, and also the ways in which linguistic systems may change over time.Aquest treball pretén fer progressos en la comprensió dels components que no són UG del model de tres factors de Chomsky (2005). En relació amb l'entrada (factor 2), argumento la necessitat de formular una hipòtesi adequada i precisa sobre quins aspectes de l'entrada es qualificaran com a "ingesta" i, per tant, seran la base de la construcció gramatical. En relació amb el factor 3, destaco un biaix cognitiu específic que apareix força motivat fora del llenguatge, alhora que té àmplies conseqüències per a la nostra comprensió de com es construeixen les gramàtiques del llenguatge I, i per què haurien de tenir la forma interlingüísticament comparable als generativistes. Es tracta de maximitzar els mitjans mínims (MMM). Demostro que la seva incorporació al nostre model d'adquisició gramatical facilita la comprensió de fets diversos sobre tipologia de llenguatge natural, adquisició, tant en contextos "estables" com "inestables", i també de les maneres de canviar els sistemes lingüístics amb el pas del temps