Attribute Weighted Na飗e Bayes Classifier

Abstract

The naïve Bayes classifier is one of the commonly used data mining methods for classification. Despite its simplicity, naïve Bayes is effective and computationally efficient. Although the strong attribute independence assumption in the naïve Bayes classifier makes it a tractable method for learning, this assumption may not hold in real-world applications. Many enhancements to the basic algorithm have been proposed in order to alleviate the violation of attribute independence assumption. While these methods improve the classification performance, they do not necessarily retain the mathematical structure of the naïve Bayes model and some at the expense of computational time. One approach to reduce the naïveté of the classifier is to incorporate attribute weights in the conditional probability. In this paper, we proposed a method to incorporate attribute weights to naïve Bayes. To evaluate the performance of our method, we used the public benchmark datasets. We compared our method with the standard naïve Bayes and baseline attribute weighting methods. Experimental results show that our method to incorporate attribute weights improves the classification performance compared to both standard naïve Bayes and baseline attribute weighting methods in terms of classification accuracy and F1, especially when the independence assumption is strongly violated, which was validated using the Chi-square test of independence.

Analisis Sentimen Produk Amazon Menggunakan Algoritma Naïve Bayes Pada Data Review Pelanggan

“…Teorema Bayes menemukan probabilitas atau kemungkinan suatu peristiwa akan terjadi dengan memberikan probabilitas peristiwa lain yang telah terjadi. Dalam istilah yang lebih sederhana, Teorema Bayes adalah metode untuk menemukan probabilitas ketika kita mengetahui probabilitas tertentu lainnya (Trivusi, 2022).Pengklasifikasi Naïve Bayes (NB) adalah keluarga pengklasifikasi probabilistik sederhana berdasarkan asumsi umum bahwa semua fitur tidak bergantung satu sama lain (Xu, 2018) Pengklasifikasi Naive Bayes adalah salah satu algoritma yang banyak digunakan untuk aplikasi penambangan data (Foo et al, 2022). Penelitian ini menggunakan metode Naïve Bayes Classifier (NBC) untuk menganalisis tingkat sentimen terhadap data yang dilakukan dengan crawling di Twitter (Normawati & Prayogi, 2021) Mendapatkan kepuasan dan kepercayaan pelanggan telah menjadi tantangan utama dalam mencapai kesuksesan di dunia bisnis.…”