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

Fokoue, Ernest. "A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining." Serdica Journal of Computing 8.2 (2014): 111-136. <http://eudml.org/doc/269898>.

@article{Fokoue2014,
abstract = {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.},
author = {Fokoue, Ernest},
journal = {Serdica Journal of Computing},
keywords = {Massive Data; Taxonomy; Parsimony; Sparsity; Regularization; Penalization; Compression; Reduction; Selection; Kernelization; Hybridization; Parallelization; Aggregation; Randomization; Sequentialization; Cross Validation; Subsampling; Bias-Variance Trade-off; Generalization; Prediction Error},
language = {eng},
number = {2},
pages = {111-136},
publisher = {Institute of Mathematics and Informatics Bulgarian Academy of Sciences},
title = {A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining},
url = {http://eudml.org/doc/269898},
volume = {8},
year = {2014},
}

TY - JOUR
AU - Fokoue, Ernest
TI - A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining
JO - Serdica Journal of Computing
PY - 2014
PB - Institute of Mathematics and Informatics Bulgarian Academy of Sciences
VL - 8
IS - 2
SP - 111
EP - 136
AB - 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.
LA - eng
KW - Massive Data; Taxonomy; Parsimony; Sparsity; Regularization; Penalization; Compression; Reduction; Selection; Kernelization; Hybridization; Parallelization; Aggregation; Randomization; Sequentialization; Cross Validation; Subsampling; Bias-Variance Trade-off; Generalization; Prediction Error
UR - http://eudml.org/doc/269898
ER -