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.

Automated Data Retrieval in Distance Learning Quality

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2015

The Positive Effect of Using the Integral Approach for Improving Quality of Learning and Students' motivation

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2015

Study of the Self-Assessment as а Component of the Model of Self-Regulated Learning for Engineering Students

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2015

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

A Bayesian Model for Learning Using Flashcards

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2015

A Computational Approach for the Statistical Estimation of Discrete Time Branching Processes with Immigration

2010 Mathematics Subject Classification: 60J80.

Automatized Generation of Personalized Learning Paths through Aspects in Multidimensional Spaces

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2016

Teacher’s Presence and Immediacy in e-Learning Classes

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2016

Game-based Approach in e-Learning

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2016

Collaborative Learning and Assessment in e-Learning

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2016

Using observation ageing to improve Markovian model learning in QoS engineering

Markovian models are widely used to analyse quality-of-service properties of both system designs and deployed systems. Thanks to the emergence of probabilistic model checkers, this analysis can be performed with high accuracy. However, its usefulness is heavily dependent on how well the model captures the actual behaviour of the analysed system. Our work addresses this problem for a class of Markovian models termed discrete-time Markov chains (DTMCs). We propose a new Bayesian technique for learning the state transition probabilities of DTMCs based on observations of the modelled system. Unlike existing approaches, our technique weighs observations based on their age, to account for the fact that older observations are less relevant than more recent ones. A case study from the area of bioinformatics workflows demonstrates the effectiveness of the technique in scenarios where the model parameters change over time.

A diszkrét kiválasztási modell becslése Cox-regresszióval (Parameter estimation for multinomial discrete choice model using Cox-regression)

Considering the so-called "multinomial discrete choice" model the focus of this paper is on the estimation problem of the parameters. Especially, the basic question arises how to carry out the point and interval estimation of the parameters when the model is mixed i.e. includes both individual and choice-specific explanatory variables while a standard MDC computer program is not available for use. The basic idea behind the solution is the use of the Cox-proportional hazards method of survival analysis which is available in any standard statistical package and provided a data structure satisfying certain special requirements it yields the MDC solutions desired. The paper describes the features of the data set to be analysed.

The effect of the use of computer assisted instruction (CAI) on attitudes and computational scores of developmental mathematics students at two inner city schools with predominantly Black enrollment

This study examined the effects of computer assisted instruction (CAI) 1 hour per week for 18 weeks on changes in computational scores and attitudes of developmental mathematics students at schools with predominantly Black enrollment. Comparisons were made between students using CAI with differing software--PLATO, CSR or both together--and students using traditional instruction (TI) only.^ This study was conducted in the Dade County Public School System from February through June 1991, at two senior high schools. The dependent variables, the State Student Assessment Test (SSAT), and the School Subjects Attitude Scales (SSAS), measured students' computational scores and attitudes toward mathematics in 3 categories: interest, usefulness, and difficulty, respectively.^ Univariate analyses of variance were performed on the least squares mean differences from pretest to posttest for testing main effects and interactions. A t-test measured significant main effects and interactions. Results were interpreted at the.01 level of significance.^ Null hypotheses 1, 2, and 3 compared versions of CAI with the control group, for changes in mathematical computation scores measured with the SSAT. It could not be concluded that changes in standardized mathematics test scores of students using CAI with differing software 1 hour per week for 18 class hours combined with TI were significantly higher than changes in test scores for students receiving TI only.^ Null hypotheses 4, 5, and 6 tested the effects of CAI for attitudes toward mathematics for experimental groups against control groups measured with the SSAS. Changes in attitudes toward mathematics of students using CAI with differing software 1 hour per week for 18 class hours combined with TI were not significantly higher than attitude changes for students receiving TI only.^ Teacher effect on students' computational scores was a more influential variable than CAI. No interaction was found between gender and learning method on standardized mathematics test scores (null hypothesis 7). ^