Correct classification for big/smart/fast data machine learning

TL;DR

This paper explores the classification of big data using Boolean function minimization, proposing a mathematical approach that transforms data representation into Boolean functions and applies known algorithms.

Contribution

It introduces a novel perspective of data classification as Boolean function minimization and demonstrates how to leverage existing algorithms for this purpose.

Findings

Data can be represented as Boolean functions for classification.

Existing Boolean minimization algorithms can be applied to data classification.

The approach facilitates development of multivalued output classifiers.

Abstract

Table (database) / Relational database Classification for big/smart/fast data machine learning is one of the most important tasks of predictive analytics and extracting valuable information from data. It is core applied technique for what now understood under data science and/or artificial intelligence. Widely used Decision Tree (Random Forest) and rare used rule based PRISM , VFST, etc classifiers are empirical substitutions of theoretically correct to use Boolean functions minimization. Developing Minimization of Boolean functions algorithms is started long time ago by Edward Veitch's 1952. Since it, big efforts by wide scientific/industrial community was done to find feasible solution of Boolean functions minimization. In this paper we propose consider table data classification from mathematical point of view, as minimization of Boolean functions. It is shown that data representation may be transformed to Boolean functions form and how to use known algorithms. For simplicity, binary output function is used for development, what opens doors for multivalued outputs developments.