English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The Era of Big Data has forced researchers to explore new distributedsolutions for building fuzzy classifiers, which often introduce approximationerrors or make strong assumptions to reduce computational and memoryrequirements. As a result, Big Data classifiers might be expected to beinferior to those designed for standard classification tasks (Small Data) interms of accuracy and model complexity. To our knowledge, however, there is noempirical evidence to confirm such a conjecture yet. Here, we investigate theextent to which state-of-the-art fuzzy classifiers for Big Data sacrificeperformance in favor of scalability. To this end, we carry out an empiricalstudy that compares these classifiers with some of the best performingalgorithms for Small Data. Assuming the latter were generally designed formaximizing performance without considering scalability issues, the results ofthis study provide some intuition around the tradeoff between performance andscalability achieved by current Big Data solutions. Our findings show that,although slightly inferior, Big Data classifiers are gradually catching up withstate-of-the-art classifiers for Small data, suggesting that a unified learningalgorithm for Big and Small Data might be possible.

Do we still need fuzzy classifiers for Small Data in the Era of Big Data?