A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining

Big data comes in various ways, types, shapes, forms and sizes. Indeed, almost all areas of science, technology, medicine, public health, economics, business, linguistics and social science are bombarded by ever increasing flows of data begging to be analyzed efficiently and effectively. In this paper, we propose a rough idea of a possible taxonomy of big data, along with some of the most commonly used tools for handling each particular category of bigness. The dimensionality p of the input space and the sample size n are usually the main ingredients in the characterization of data bigness. The specific statistical machine learning technique used to handle a particular big data set will depend on which category it falls in within the bigness taxonomy. Large p small n data sets for instance require a different set of tools from the large n small p variety. Among other tools, we discuss Preprocessing, Standardization, Imputation, Projection, Regularization, Penalization, Compression, Reduction, Selection, Kernelization, Hybridization, Parallelization, Aggregation, Randomization, Replication, Sequentialization. Indeed, it is important to emphasize right away that the so-called no free lunch theorem applies here, in the sense that there is no universally superior method that outperforms all other methods on all categories of bigness. It is also important to stress the fact that simplicity in the sense of Ockham’s razor non-plurality principle of parsimony tends to reign supreme when it comes to massive data. We conclude with a comparison of the predictive performance of some of the most commonly used methods on a few data sets.

A Comparative Analysis of Predictive Learning Algorithms on High-Dimensional Microarray Cancer Data

This research evaluates pattern recognition techniques on a subclass of big data where the dimensionality of the input space (p) is much larger than the number of observations (n). Specifically, we evaluate massive gene expression microarray cancer data where the ratio κ is less than one. We explore the statistical and computational challenges inherent in these high dimensional low sample size (HDLSS) problems and present statistical machine learning methods used to tackle and circumvent these difficulties. Regularization and kernel algorithms were explored in this research using seven datasets where κ < 1. These techniques require special attention to tuning necessitating several extensions of cross-validation to be investigated to support better predictive performance. While no single algorithm was universally the best predictor, the regularization technique produced lower test errors in five of the seven datasets studied.

Teaching Statistics to Engineers: Learning from Experiential Data

The purpose of the work is to claim that engineers can be motivated to study statistical concepts by using the applications in their experience connected with Statistical ideas. The main idea is to choose a data from the manufacturing factility (for example, output from CMM machine) and explain that even if the parts used do not meet exact specifications they are used in production. By graphing the data one can show that the error is random but follows a distribution, that is, there is regularily in the data in statistical sense. As the error distribution is continuous, we advocate that the concept of randomness be introducted starting with continuous random variables with probabilities connected with areas under the density. The discrete random variables are then introduced in terms of decision connected with size of the errors before generalizing to abstract concept of probability. Using software, they can then be motivated to study statistical analysis of the data they encounter and the use of this analysis to make engineering and management decisions.

How Students Study? An Analysis of Learning Strategies in Engineering and Technology Courses

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

The effect of cooperative learning on the attitudes toward science and the achievement of students in a non-science majors' general biology laboratory course at an urban community college

The purpose of this study was to evaluate the effect of cooperative learning strategies on students' attitudes toward science and achievement in BSC 1005L, a non-science majors' general biology laboratory course at an urban community college. Data were gathered on the participants' attitudes toward science and cognitive biology level pre and post treatment in BSC 1005L. Elements of the Learning Together model developed by Johnson and Johnson and the Student Team-Achievement Divisions model created by Slavin were incorporated into the experimental sections of BSC 1005L.^ Four sections of BSC 1005L participated in this study. Participants were enrolled in the 1998 spring (January) term. Students met weekly in a two hour laboratory session. The treatment was administered to the experimental group over a ten week period. A quasi-experimental pretest-posttest control group design was used. Students in the cooperative learning group (n$\sb1$ = 27) were administered the Test of Science-Related Attitudes (TOSRA) and the cognitive biology test at the same time as the control group (n$\sb2$ = 19) (at the beginning and end of the term).^ Statistical analyses confirmed that both groups were equivalent regarding ethnicity, gender, college grade point average and number of absences. Independent sample t-tests performed on pretest mean scores indicated no significant differences in the TOSRA scale two or biology knowledge between the cooperative learning group and the control group. The scores of TOSRA scales: one, three, four, five, six, and seven were significantly lower in the cooperative learning group. Independent sample t-tests of the mean score differences did not show any significant differences in posttest attitudes toward science or biology knowledge between the two groups. Paired t-tests did not indicate any significant differences on the TOSRA or biology knowledge within the cooperative learning group. Paired t-tests did show significant differences within the control group on TOSRA scale two and biology knowledge. ANCOVAs did not indicate any significant differences on the post mean scores of the TOSRA or biology knowledge adjusted by differences in the pretest mean scores. Analysis of the research data did not show any significant correlation between attitudes toward science and biology knowledge. ^

Group presence in virtual worlds: Supporting collaborative e-learning

Even though e-learning endeavors have significantly proliferated in recent years, current e-learning technologies provide poor support for group-oriented learning. The now popular virtual world's technologies offer a possible solution. Virtual worlds provide the users with a 3D - computer generated shared space in which they can meet and interact through their virtual representations. Virtual worlds are very successful in developing high levels of engagement, presence and group presence in the users. These elements are also desired in educational settings since they are expected to enhance performance. The goal of this research is to test the hypothesis that a virtual world learning environment provides better support for group-oriented collaborative e-learning than other learning environments, because it facilitates the emergence of group presence. To achieve this, a quasi-experimental study was conducted and data was gathered through the use of various survey instruments and a set of collaborative tasks assigned to the participants. Data was gathered on the dependent variables: Engagement, Group Presence, Individual Presence, Perceived Individual Presence, Perceived Group Presence and Performance. The data was analyzed using the statistical procedures of Factor Analysis, Path Analysis, Analysis of Variance (ANOVA) and Multivariate Analysis of Variance (MANOVA). The study provides support for the hypothesis. The results also show that virtual world learning environments are better than other learning environments in supporting the development of all the dependent variables. It also shows that while only Individual Presence has a significant direct effect on Performance; it is highly correlated with both Engagement and Group Presence. This suggests that these are also important in regards to performance. Developers of e-learning endeavors and educators should incorporate virtual world technologies in their efforts in order to take advantage of the benefit they provide for e-learning group collaboration.

Statistical dialog management for health interventions

Research endeavors on spoken dialogue systems in the 1990s and 2000s have led to the deployment of commercial spoken dialogue systems (SDS) in microdomains such as customer service automation, reservation/booking and question answering systems. Recent research in SDS has been focused on the development of applications in different domains (e.g. virtual counseling, personal coaches, social companions) which requires more sophistication than the previous generation of commercial SDS. The focus of this research project is the delivery of behavior change interventions based on the brief intervention counseling style via spoken dialogue systems. ^ Brief interventions (BI) are evidence-based, short, well structured, one-on-one counseling sessions. Many challenges are involved in delivering BIs to people in need, such as finding the time to administer them in busy doctors' offices, obtaining the extra training that helps staff become comfortable providing these interventions, and managing the cost of delivering the interventions. Fortunately, recent developments in spoken dialogue systems make the development of systems that can deliver brief interventions possible. ^ The overall objective of this research is to develop a data-driven, adaptable dialogue system for brief interventions for problematic drinking behavior, based on reinforcement learning methods. The implications of this research project includes, but are not limited to, assessing the feasibility of delivering structured brief health interventions with a data-driven spoken dialogue system. Furthermore, while the experimental system focuses on harmful alcohol drinking as a target behavior in this project, the produced knowledge and experience may also lead to implementation of similarly structured health interventions and assessments other than the alcohol domain (e.g. obesity, drug use, lack of exercise), using statistical machine learning approaches. ^ In addition to designing a dialog system, the semantic and emotional meanings of user utterances have high impact on interaction. To perform domain specific reasoning and recognize concepts in user utterances, a named-entity recognizer and an ontology are designed and evaluated. To understand affective information conveyed through text, lexicons and sentiment analysis module are developed and tested.^

Learning and Exploiting Camera Geometry for Computer Vision

This work explores the use of statistical methods in describing and estimating camera poses, as well as the information feedback loop between camera pose and object detection. Surging development in robotics and computer vision has pushed the need for algorithms that infer, understand, and utilize information about the position and orientation of the sensor platforms when observing and/or interacting with their environment.

The first contribution of this thesis is the development of a set of statistical tools for representing and estimating the uncertainty in object poses. A distribution for representing the joint uncertainty over multiple object positions and orientations is described, called the mirrored normal-Bingham distribution. This distribution generalizes both the normal distribution in Euclidean space, and the Bingham distribution on the unit hypersphere. It is shown to inherit many of the convenient properties of these special cases: it is the maximum-entropy distribution with fixed second moment, and there is a generalized Laplace approximation whose result is the mirrored normal-Bingham distribution. This distribution and approximation method are demonstrated by deriving the analytical approximation to the wrapped-normal distribution. Further, it is shown how these tools can be used to represent the uncertainty in the result of a bundle adjustment problem.

Another application of these methods is illustrated as part of a novel camera pose estimation algorithm based on object detections. The autocalibration task is formulated as a bundle adjustment problem using prior distributions over the 3D points to enforce the objects' structure and their relationship with the scene geometry. This framework is very flexible and enables the use of off-the-shelf computational tools to solve specialized autocalibration problems. Its performance is evaluated using a pedestrian detector to provide head and foot location observations, and it proves much faster and potentially more accurate than existing methods.

Finally, the information feedback loop between object detection and camera pose estimation is closed by utilizing camera pose information to improve object detection in scenarios with significant perspective warping. Methods are presented that allow the inverse perspective mapping traditionally applied to images to be applied instead to features computed from those images. For the special case of HOG-like features, which are used by many modern object detection systems, these methods are shown to provide substantial performance benefits over unadapted detectors while achieving real-time frame rates, orders of magnitude faster than comparable image warping methods.

The statistical tools and algorithms presented here are especially promising for mobile cameras, providing the ability to autocalibrate and adapt to the camera pose in real time. In addition, these methods have wide-ranging potential applications in diverse areas of computer vision, robotics, and imaging.

Machine Learning-based Techniques to Address Spectral Distortions in Photon Counting X-ray Computed Tomography

Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast agents to address the otherwise limited contrast in soft tissues. We have developed a micro-CT system based on a PCXD. This system enables full spectrum CT in which the energy thresholds of the PCXD are swept to sample the full energy spectrum for each detector element and projection angle. Measurements provided by the PCXD, however, are distorted due to undesirable physical eects in the detector and are very noisy due to photon starvation. In this work, we proposed two methods based on machine learning to address the spectral distortion issue and to improve the material decomposition. This rst approach is to model distortions using an articial neural network (ANN) and compensate for the distortion in a statistical reconstruction. The second approach is to directly correct for the distortion in the projections. Both technique can be done as a calibration process where the neural network can be trained using 3D printed phantoms data to learn the distortion model or the correction model of the spectral distortion. This replaces the need for synchrotron measurements required in conventional technique to derive the distortion model parametrically which could be costly and time consuming. The results demonstrate experimental feasibility and potential advantages of ANN-based distortion modeling and correction for more accurate K-edge imaging with a PCXD. Given the computational eciency with which the ANN can be applied to projection data, the proposed scheme can be readily integrated into existing CT reconstruction pipelines.

Statistical Dialog Management for Health Interventions

Research endeavors on spoken dialogue systems in the 1990s and 2000s have led to the deployment of commercial spoken dialogue systems (SDS) in microdomains such as customer service automation, reservation/booking and question answering systems. Recent research in SDS has been focused on the development of applications in different domains (e.g. virtual counseling, personal coaches, social companions) which requires more sophistication than the previous generation of commercial SDS. The focus of this research project is the delivery of behavior change interventions based on the brief intervention counseling style via spoken dialogue systems. Brief interventions (BI) are evidence-based, short, well structured, one-on-one counseling sessions. Many challenges are involved in delivering BIs to people in need, such as finding the time to administer them in busy doctors' offices, obtaining the extra training that helps staff become comfortable providing these interventions, and managing the cost of delivering the interventions. Fortunately, recent developments in spoken dialogue systems make the development of systems that can deliver brief interventions possible. The overall objective of this research is to develop a data-driven, adaptable dialogue system for brief interventions for problematic drinking behavior, based on reinforcement learning methods. The implications of this research project includes, but are not limited to, assessing the feasibility of delivering structured brief health interventions with a data-driven spoken dialogue system. Furthermore, while the experimental system focuses on harmful alcohol drinking as a target behavior in this project, the produced knowledge and experience may also lead to implementation of similarly structured health interventions and assessments other than the alcohol domain (e.g. obesity, drug use, lack of exercise), using statistical machine learning approaches. In addition to designing a dialog system, the semantic and emotional meanings of user utterances have high impact on interaction. To perform domain specific reasoning and recognize concepts in user utterances, a named-entity recognizer and an ontology are designed and evaluated. To understand affective information conveyed through text, lexicons and sentiment analysis module are developed and tested.

Digital anthropology and educational eGames: learning through behavioural patterns in digital, game-based contexts

The selected publications are focused on the relations between users, eGames and the educational context, and how they interact together, so that both learning and user performance are improved through feedback provision. A key part of this analysis is the identification of behavioural, anthropological patterns, so that users can be clustered based on their actions, and the steps taken in the system (e.g. social network, online community, or virtual campus). In doing so, we can analyse large data sets of information made by a broad user sample,which will provide more accurate statistical reports and readings. Furthermore, this research is focused on how users can be clustered based on individual and group behaviour, so that a personalized support through feedback is provided, and the personal learning process is improved as well as the group interaction. We take inputs from every person and from the group they belong to, cluster the contributions, find behavioural patterns and provide personalized feedback to the individual and the group, based on personal and group findings. And we do all this in the context of educational games integrated in learning communities and learning management systems. To carry out this research we design a set of research questions along the 10-year published work presented in this thesis. We ask if the users can be clustered together based on the inputs provided by them and their groups; if and how these data are useful to improve the learner performance and the group interaction; if and how feedback becomes a useful tool for such pedagogical goal; if and how eGames become a powerful context to deploy the pedagogical methodology and the various research methods and activities that make use of that feedback to encourage learning and interaction; if and how a game design and a learning design must be defined and implemented to achieve these objectives, and to facilitate the productive authoring and integration of eGames in pedagogical contexts and frameworks. We conclude that educational games are a resourceful tool to provide a user experience towards a better personalized learning performance and an enhance group interaction along the way. To do so, eGames, while integrated in an educational context, must follow a specific set of user and technical requirements, so that the playful context supports the pedagogical model underneath. We also conclude that, while playing, users can be clustered based on their personal behaviour and interaction with others, thanks to the pattern identification. Based on this information, a set of recommendations are provided Digital Anthropology and educational eGames 6 /216 to the user and the group in the form of personalized feedback, timely managed for an optimum impact on learning performance and group interaction level. In this research, Digital Anthropology is introduced as a concept at a late stage to provide a backbone across various academic fields including: Social Science, Cognitive Science, Behavioural Science, Educational games and, of course, Technology-enhance learning. Although just recently described as an evolution of traditional anthropology, this approach to digital behaviour and social structure facilitates the understanding amongst fields and a comprehensive view towards a combined approach. This research takes forward the already existing work and published research onusers and eGames for learning, and turns the focus onto the next step — the clustering of users based on their behaviour and offering proper, personalized feedback to the user based on that clustering, rather than just on isolated inputs from every user. Indeed, this pattern recognition in the described context of eGames in educational contexts, and towards the presented aim of personalized counselling to the user and the group through feedback, is something that has not been accomplished before.

Performance Evaluation of a Statistical and a Neural Network Model for Nonrigid Shape-Based Registration

Shape-based registration methods frequently encounters in the domains of computer vision, image processing and medical imaging. The registration problem is to find an optimal transformation/mapping between sets of rigid or nonrigid objects and to automatically solve for correspondences. In this paper we present a comparison of two different probabilistic methods, the entropy and the growing neural gas network (GNG), as general feature-based registration algorithms. Using entropy shape modelling is performed by connecting the point sets with the highest probability of curvature information, while with GNG the points sets are connected using nearest-neighbour relationships derived from competitive hebbian learning. In order to compare performances we use different levels of shape deformation starting with a simple shape 2D MRI brain ventricles and moving to more complicated shapes like hands. Results both quantitatively and qualitatively are given for both sets.

Virtual Reality as a tool to Assist Learning in Aerospace Assembly

This study examines whether virtual reality (VR) is more superior to paper-based instructions in increasing the speed at which individuals learn a new assembly task. Specifically, the work seeks to quantify any learning benefits when individuals have been given the opportunity and compares the performance of two groups using virtual and hardcopy media types to pre-learn the task. A build experiment based on multiple builds of an aircraft panel showed that a group of people who pre-learned the assembly task using a VR environment completed their builds faster (average build time 29.5% lower). The VR group also made fewer references to instructional materials (average number of references 38% lower) and made fewer errors than a group using more traditional, hard copy instructions. These outcomes were more pronounced during build one with differences in build time and number of references showing limited statistical differences.

Machine learning and data decompositions for complex networked dynamical systems

Thesis (Ph.D.)--University of Washington, 2016-08

Essays on Machine Learning and Hedonic Models

Thesis (Ph.D.)--University of Washington, 2016-08