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No / Microarray data analysis and classification has demonstrated convincingly that it provides an effective methodology for the effective diagnosis of diseases and cancers. Although much research has been performed on applying machine learning techniques for microarray data classification during the past years, it has been shown that conventional machine learning techniques have intrinsic drawbacks in achieving accurate and robust classifications. This paper presents a novel ensemble machine learning approach for the development of robust microarray data classification. Different from the conventional ensemble learning techniques, the approach presented begins with generating a pool of candidate base classifiers based on the gene sub-sampling and then the selection of a sub-set of appropriate base classifiers to construct the classification committee based on classifier clustering. Experimental results have demonstrated that the classifiers constructed by the proposed method outperforms not only the classifiers generated by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods (bagging and boosting).

This dissertation develops a new method of modeling uncertainty with spatial data called Bayesian spatial predictive synthesis (BSPS) and compares its predictive accuracy to established methods. Spatial data are often non-linear, complex, and difficult to capture with a single model. Existing methods such as model selection or simple model ensembling fail to consider the critical spatially varying model uncertainty problem; different models perform better or worse in different regions. BSPS can capture the model uncertainty by specifying a latent factor coefficient model that varies spatially as a synthesis function. This allows the model coefficients to vary across a region to achieve flexible spatial model ensembling. This method is derived from the theoretically best approximation of the data generating process (DGP), where the predictions are exact minimax. Two Markov chain Monte Carlo (MCMC) based algorithms are implemented in the BSPS framework for full uncertainty quantification, along with a variational Bayes strategy for faster point inference. This method is also extended for general responses. The examples in this dissertation include multiple simulation studies and two real world data applications. Through these examples, the performance and predictive power of BSPS is shown against various standard spatial models, ensemble methods, and machine learning methods. BSPS is able to maintain predictive accuracy as well as maintain interpretability of the prediction mechanisms. / Statistics

Tree canopy cover is an essential measure of forest health and productivity, which is widely studied due to its relevance to many disciplines. For example, declining tree canopy cover can be an indicator of forest health, insect infestation, or disease. This dissertation consists of three studies, focused on the spatial modeling and evaluation of tree canopy cover, drawing on recent developments and best practices in the fields of remote sensing, data collection, and statistical analysis.newlinenewline The first study evaluates how well harmonic regression variables derived at the pixel-level using a time-series of all available Landsat images predict values of tree canopy cover. Harmonic regression works to approximate the reflectance curve of a given band across time. Therefore the coefficients that result from the harmonic regression model estimate relate to the phenology of the area of each pixel. We use a time-series of all available cloud-free observations in each Landsat pixel for NDVI, SWIR1 and SWIR2 bands to obtain harmonic regression coefficients for each variable and then use those coefficients to estimate tree canopy cover at two discrete points in time. This study compares models estimated using these harmonic regression coefficients to those estimated using Landsat median composite imagery, and combined models. We show that (1) harmonic regression coefficients that use a single harmonic coefficient provided the best quality models, (2) harmonic regression coefficients from Landsat-derived NDVI, SWIR1, and SWIR2 bands improve the quality of tree canopy cover models when added to the full suite of median composite variables, (3) the harmonic regression constant for the NDVI time-series is an important variable across models, and (4) there is little to no additional information in the full suite of predictors compared to the harmonic regression coefficients alone based on the information criterion provided by principal components analysis. The second study presented evaluates the use of crowdsourcing with Amazon's Mechanical Turk platform to obtain photointerpretated tree canopy cover data. We collected multiple interpretations at each plot from both crowd and expert interpreters, and sampled these data using a Monte Carlo framework to estimate a classification model predicting the "reliability" of each crowd interpretation using expert interpretations as a benchmark, and identified the most important variables in estimating this reliability. The results show low agreement between crowd and expert groups, as well as between individual experts. We found that variables related to fatigue had the most bearing on the "reliability" of crowd interpretations followed by whether the interpreter used false color or natural color composite imagery during interpretation. Recommendations for further study and future implementations of crowdsourced photointerpretation are also provided. In the final study, we explored sampling methods for the purpose of model validation. We evaluated a method of stratified random sampling with optimal allocation using measures of prediction uncertainty derived from random forest regression models by comparing the accuracy and precision of estimates from samples drawn using this method to estimates from samples drawn using other common sampling protocols using three large, simulated datasets as case studies. We further tested the effect of reduced sample sizes on one of these datasets and demonstrated a method to report the accuracy of continuous models for domains that are either regionally constrained or numerically defined based on other variables or the modeled quantity itself. We show that stratified random sampling with optimal allocation provides the most precise estimates of the mean of the reference Y and the RMSE of the population. We also demonstrate that all sampling methods provide reasonably accurate estimates on average. Additionally we show that, as sample sizes are increased with each sampling method, the precision generally increases, eventually reaching a level of convergence where gains in estimate precision from adding additional samples would be marginal. / Doctor of Philosophy / Tree canopy cover is an essential measure of forest health, which is widely studied due to its relevance to many disciplines. For example, declining tree canopy cover can be an indicator of forest health, insect infestation, or disease. This dissertation consists of three studies, focused on the spatial modeling and evaluation of tree canopy cover, drawing on recent developments and best practices in the fields of remote sensing, data collection, and statistical analysis. The first study is an evaluation of the utility of harmonic regression coefficients from time-series satellite imagery, which describe the timing and magnitude of green-up and leaf loss at each location, to estimate tree canopy cover. This study compares models estimated using these harmonic regression coefficients to those estimated using median composite imagery, which obtain the median value of reflectance values across time data at each location, and models which used both types of variables. We show that (1) harmonic regression coefficients that use a simplified formula provided higher quality models compared to more complex alternatives, (2) harmonic regression coefficients improved the quality of tree canopy cover models when added to the full suite of median composite variables, (3) the harmonic regression constant, which is the coefficient that determines the average reflectance over time, based on time-series vegetation index data, is an important variable across models, and (4) there is little to no additional information in the full suite of predictors compared to the harmonic regression coefficients alone.newlinenewline The second study presented, evaluates the use of crowdsourcing, which engages non-experts in paid online tasks, with Amazon's Mechanical Turk platform to obtain tree canopy cover data, as interpreted from aerial images. We collected multiple interpretations at each location from both crowd and expert interpreters, and sampled these data using a repeated sampling framework to estimate a classification model predicting the "reliability" of each crowd interpretation using expert interpretations as a benchmark, and identified the most important variables in estimating this "reliability". The results show low agreement between crowd and expert groups, as well as between individual experts. We found that variables related to fatigue had the most bearing on the reliability of crowd interpretations followed by variables related to the display settings used to view imagery during interpretation. Recommendations for further study and future implementations of crowdsourced photointerpretation are also provided. In the final study, we explored sampling methods for the purpose of model validation. We evaluated a method of stratified random sampling with optimal allocation, a sampling method that is specifically designed to improve the precision of sample estimates, using measures of prediction uncertainty, describing the variability in predictions from different models in an ensemble of regression models. We compared the accuracy and precision of estimates from samples drawn using this method to estimates from samples drawn using other common sampling protocols using three large, mathematically simulated data products as case studies. We further tested the effect of smaller sample sizes on one of these data products and demonstrated a method to report the accuracy of continuous models for different land cover classes and for classes defined using 10% tree canopy cover intervals. We show that stratified random sampling with optimal allocation provides the most precise sample estimates. We also demonstrate that all sampling methods provide reasonably accurate estimates on average and we show that, as sample sizes are increased with each sampling method, the precision generally increases, eventually leveling off where gains in estimate precision from adding additional samples would be marginal.

Constellations is a single movement work that explores the color between acoustic instruments and computer generated sounds. It is scored for four flutes (two doubling on piccolos), two oboes, two bassoons, eight B-flat clarinets, two bass B-flat clarinets; two alto saxophones, tenor saxophone, baritone saxophone; four trumpets in B-flat, four horns in F; three trombones, bass trombone; two tubas;piano; six percussionists; and contrabass. The duration of the work is nine minutes and twenty-eight seconds. Mapping of stellar constellations provide the primary material for all pitch and harmonic progressions throughout the work. Software synthesis and digital sampling techniques coalesce to produce the computer music on tape.

Abstract : The objective is to provide methods to improve the performance, or prediction accuracy of standard stacking approach, which is an ensemble method composed of simple, heterogeneous base models, through the integration of the diversity generation, combination and/or selection stages for regression problems. In Chapter 1, we propose to combine a set of level-1 learners into a level-2 learner, or ensemble. We also propose to inject a diversity generation mechanism into the initial cross-validation partition, from which new cross-validation partitions are generated, and sub-sequent ensembles are trained. Then, we propose an algorithm to select best partition, or corresponding ensemble. In Chapter 2, we formulate the partition selection as a Pareto-based multi-criteria optimization problem, as well as an algorithm to make the partition selection iterative with the aim to improve more the ensemble prediction accuracy. In Chapter 3, we propose to generate multiple populations or partitions by injecting a diversity mechanism to the original dataset. Then, an algorithm is proposed to select the best partition among all partitions generated by the multiple populations. All methods designed and implemented in this thesis get encouraging, and favorably results across different dataset against both state-of-the-art models, and ensembles for regression. / Résumé : L’objectif est de fournir des techniques permettant d’améliorer la performance de l’algorithme de stacking, une méthode ensembliste composée de modèles de base simples et hétérogènes, à travers l’intégration de la génération de la diversité, la sélection et combinaison des modèles. Dans le chapitre 1, nous proposons de combiner différents sous-ensembles de modèles de base obtenus au primer niveau. Nous proposons un mécanisme pour injecter de la diversité dans la partition croisée initiale, à partir de laquelle de nouvelles partitions de validation croisée sont générées, et les ensembles correspondant sont formés. Ensuite, nous proposons un algorithme pour sélectionner la meilleure partition. Dans le chapitre 2, nous formulons la sélection de la partition comme un problème d’optimisation multi-objectif fondé sur un principe de Pareto, ainsi que d’un algorithme pour faire une application itérative de la sélection avec l’objectif d’améliorer d’avantage la précision d’ensemble. Dans le chapitre 3, nous proposons de générer plusieurs populations en injectant un mécanisme de diversité à l’ensemble de données original. Ensuite, un algorithme est proposé pour sélectionner la meilleur partition entre toutes les partitions produite par les multiples populations. Nous avons obtenu des résultats encourageants avec ces algorithmes lors de comparaisons avec des modèles reconnus sur plusieurs bases de données.

Syftet med studien är att definiera musikaliska redskap och plattformar för studenter i ämnet improvisation på eftergymnasial nivå. Mitt intresse för ämnet ligger i de positiva möjligheter improvisationen erbjuder vår undervisning och är utgångspunkten för detta arbete. Jag upplever att forskning som inriktar sig på improvisationsundervisningens didaktik för ensemble på högre nivå med fördel kan utökas från vad vi idag har att tillgå, därav utgår studien från ett didaktiskt perspektiv på undervisning.   För att söka svar på mina frågor har jag använt mig av den kvalitativa forskningsintervjun i mötet med mina informanter. De fyra utvalda musiklärarna har alla undervisat improvisation i ensembleformat, de flesta med inriktning mot rytmisk och improviserad musik.   I resultatet presenteras bland annat en syn hos de intervjuade musiklärarna som visar på vikten av ett personligt och ärligt musikutövande där den instrumentaltekniska färdigheten först får liv och mening då studenten har en konstnärlig åsikt att förmedla. Sedan diskuteras bland annat lärarnas kompetens som improvisationspedagoger i relation till Hanken och Johansens (1998) musikdidaktiska utgångspunkter, samt ämnets möjliga utveckling och framtida undervisningsstruktur. / The study aims to define musical tools and platforms for students in the subject of improvisation at the collegiate level. My interest in the subject, and starting point for this work, lies in the positive benefits of working with improvisation. I feel that research today, focused on teaching improvisation for ensemble at a higher level can be extended from what is currently available. I have therefore chosen didactics as my theoretical basis.   In search of answers to my questions, I am using the qualitative research interview method with my informants. The four selected informants have all been teaching improvisation in ensemble, most with a focus on rhythmic and improvised music.   The results are presented including a view of the interviewed music teachers on the importance of a personal and candid music performance in which instrumental technique emerges when the student has an artistic opinion to impart. Thereafter, the study will discuss the teachers' pedagogical skills as improvisation teachers in relation to Hanken and Johansen's (1998) music didactic viewpoints, and possible development and future educational structure for the subject of improvisation.

Syftet med denna undersökning är att ta reda på om och i vilken grad elever, och lärare i ensemble och musikteori, upplever att eleverna integrerar musikteori och ensemble samt ifall bakgrund i musik- eller kulturskola påverkade integrationen. Jag använde mig av en kvantitativ undersökningsmetod, det vill säga enkätundersökning i mitt datainsamlande. Fyra skolor i olika storlek och olika delar av Sverige deltog i undersökning som genomfördes med ett ”emic” insider- och ”epic” outsiderperspektiv, det vill säga elev- och lärarperspektiv.I min bakgrund visar jag att musiklära kurs A enligt kursplanen ska ligga till grund för det egna musicerandet. Ensemblekursen har främst för avsikt att ge grundläggande färdigheter relaterade till sång eller instrument, men påvisar koppling i B-kursen. Rostvall & West skriver om kognitiva scheman för att lära in nya saker, och beskriver hur man kan gå igenom dem på olika sätt för att få djupare kunskap. Flera av examensarbetena jag läst visar att vikten av integration mellan teori och praktik skiljer beroende på vilken genre man spelar.Resultatet visar att elever i storstadsområden i högre grad än de i glesbygden gått i musik- eller kulturskola innan gymnasiet, samt att elever med förutbildning håller teoriintegration i det praktiska musicerandet högre än de andra. De flesta eleverna upplever sig göra kopplingen mellan teori och praktik i relativt stor utsträckning, medan lärarna upplever att de gör detta ganska lite. De flesta lärarna upplever även att kunskapsnivån på eleverna sjunker varje år. Teorilärarna hoppas på mer koppling till praktiken i framtiden och eleverna efterfrågar mer koppling till sina egna instrument i teoriundervisningen. Ensemblelärarna tror att undervisningen i framtiden mer kommer att utgå ifrån elevernas musik och bli mer gehörsbaserad. / The purpose of this study was to discern the extent of how students and teachers of ensemble and music theory, regard the integration of the students’ knowledge of music theory and ensemble, and if a background in music school affected the integration. I used a quantitative survey method. Four schools of different sizes and different parts of Sweden participated in the survey conducted by a "emic" inside and "epic" outsider perspective, that is, student and teacher perspectives.In my background, I show that music theory A according to curriculum will form the basis for their musicianship. The ensemble course is primarily intended to provide basic skills related to their vocals or instruments, but showing a link to theory the B-course. Rostvall & West write about cognitive schedules, and describe how to teach something in different ways for a deeper learning. Several of the works I have read show that integration is greatly affected by each individual genre.The results show that students in metropolitan areas to a greater extent than those in rural areas have attended music schools before high school, and students who already possess a basic understanding of theory place a greater value of its inclusion in their playing. Most students feel they make the connection between theory and practice in a relatively large scale, while teachers feel the extent is rather small. Most teachers also feel that the knowledge level of students drops each year. Music theory teachers would prefer a greater connection between theory and performance in the future, and students would like more access to their own instruments in the music theory classroom. Ensemble teachers are of the opinion that teaching in the future will be based more on the students’ repertoire, with added emphasis on aural skills.

Föreliggande arbete inriktar sig på lärares sätt att kommunicera i undervisning av ensemble där de bemöter flera elever samtidigt. Med hjälp av videoobservationer av fyra lärare som bedriver undervisning av mindre ensembler på gymnasiet har jag analyserat fram vilka kommunikationssätt som används samt hur de används. Resultatet visar att lärarna använder sig av så väl verbal som icke-verbal kommunikation i sin undervisning. Via flertalet multimodala och semiotiska resurser kommunicerar musiklärarna med sina elever under lektionens gång. De kommunikationsspråk som förekommer är ett socialt, humoristiskt, auktoritärt, gestaltande, kunnigt och målande språk. I diskussionen tar jag bland annat upp hur lärarna i undersökningen valt att designa sin undervisning utifrån situationerna om spel och i spel, samt vad detta ger för förutsättningar för dem själva och deras elever. / The study focuses on how teachers communicate when teaching music ensembles in which they meet several students simultaneously. With the help of recorded video lessons of four teachers while teaching small ensembles at the high school level, I have analysed the means of communication and how these are applied. The results show that teachers use verbal as well as nonverbal communication in their teaching. They communicate with their students during the lesson via several multimodal semiotic resources. The language of communication that results is a social, humorous, authoritative, creative, knowledgeable and vivid language. In the chapter on discussion, I present among other things, how the teachers in the study chose to design their education based on the situations regarding performance and through performance, and the conditions thus created for themselves and their students.