FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification

TL;DR

This paper presents FSL-BM, a real-time fuzzy supervised learning algorithm using binary meta-features, Hamming Distance, and hash functions to improve classification speed and accuracy in big data contexts.

Contribution

It introduces a novel real-time fuzzy supervised learning method leveraging binary meta-features, hash tables, and Hamming Distance for improved efficiency and scalability.

Findings

Achieves faster training and validation through hash table indexing.

Provides comparable or better accuracy than existing fuzzy supervised algorithms.

Reduces assumptions and time complexity in big data classification.

Abstract

This paper introduces a novel real-time Fuzzy Supervised Learning with Binary Meta-Feature (FSL-BM) for big data classification task. The study of real-time algorithms addresses several major concerns, which are namely: accuracy, memory consumption, and ability to stretch assumptions and time complexity. Attaining a fast computational model providing fuzzy logic and supervised learning is one of the main challenges in the machine learning. In this research paper, we present FSL-BM algorithm as an efficient solution of supervised learning with fuzzy logic processing using binary meta-feature representation using Hamming Distance and Hash function to relax assumptions. While many studies focused on reducing time complexity and increasing accuracy during the last decade, the novel contribution of this proposed solution comes through integration of Hamming Distance, Hash function, binary meta-features, binary classification to provide real time supervised method. Hash Tables (HT) component gives a fast access to existing indices; and therefore, the generation of new indices in a constant time complexity, which supersedes existing fuzzy supervised algorithms with better or comparable results. To summarize, the main contribution of this technique for real-time Fuzzy Supervised Learning is to represent hypothesis through binary input as meta-feature space and creating the Fuzzy Supervised Hash table to train and validate model.