RFM (Recency, Frequency and Monetary) model has been widely applied in many practical
areas in a long history, particularly in direct marketing. By adopting RFM model, decision …

Self-supervised learning has been shown to be very effective in learning useful
representations, and yet much of the success is achieved in data types such as images …

Abstract Machine learning classifiers perform well on balanced datasets. Unfortunately, a lot
of the real-world data sets are naturally imbalanced. So, imbalanced classification is a …

We present a new algorithm for differentially private data release, based on a simple
combination of the Exponential Mechanism with the Multiplicative Weights update rule. Our …

This paper employs the recently proposed nature-inspired algorithm called Multi-Verse
Optimizer (MVO) for training the Multi-layer Perceptron (MLP) neural network. The new …

Wearable devices generate different types of physiological data about the individuals. These
data can provide valuable insights for medical researchers and clinicians that cannot be …

Abstract Nowadays, Machine Learning (ML) models are becoming ubiquitous in today's
society, supporting people with their day-to-day decisions. In this context, Explainable ML is …

Purpose The purpose of this paper is to propose a new RFM model called length, recency,
frequency, monetary and periodicity (LRFMP) for classifying customers in the grocery retail …

Class imbalance is a major issue that degrades the performance of machine learning
classifiers in real-world problems. Oversampling methods have been widely used to …

This article introduces a new dimension, Interpurchase Time (T), into the existing RFM
(Recency, Frequency, and Monetary) model to form an expanded RFMT model for parsing …