Extension OL-MDISF: Online Learning from Mix-Typed, Drifted, and Incomplete Streaming Features

TL;DR

This paper introduces OL-MDISF, an advanced online learning algorithm capable of handling mixed feature types, concept drift, and incomplete supervision in streaming data, with theoretical backing and extensive experimental validation.

Contribution

It presents a novel algorithm that relaxes restrictions on feature heterogeneity, drift detection, and supervision, advancing online learning in complex real-world scenarios.

Findings

Effective detection of concept drift using adaptive sliding windows

Robust modeling of mixed feature types with copula models

Demonstrated superior performance across 14 real-world datasets

Abstract

Online learning, where feature spaces can change over time, offers a flexible learning paradigm that has attracted considerable attention. However, it still faces three significant challenges. First, the heterogeneity of real-world data streams with mixed feature types presents challenges for traditional parametric modeling. Second, data stream distributions can shift over time, causing an abrupt and substantial decline in model performance. Additionally, the time and cost constraints make it infeasible to label every data instance in a supervised setting. To overcome these challenges, we propose a new algorithm Online Learning from Mix-typed, Drifted, and Incomplete Streaming Features (OL-MDISF), which aims to relax restrictions on both feature types, data distribution, and supervision information. Our approach involves utilizing copula models to create a comprehensive latent space, employing an adaptive sliding window for detecting drift points to ensure model stability, and establishing label proximity information based on geometric structural relationships. To demonstrate the model's efficiency and effectiveness, we provide theoretical analysis and comprehensive experimental results. This extension serves as a standalone technical reference to the original OL-MDISF method. It provides (i) a contextual analysis of OL-MDISF within the broader landscape of online learning, covering recent advances in mixed-type feature modeling, concept drift adaptation, and weak supervision, and (ii) a comprehensive set of experiments across 14 real-world datasets under two types of drift scenarios. These include full CER trends, ablation studies, sensitivity analyses, and temporal ensemble dynamics. We hope this document can serve as a reproducible benchmark and technical resource for researchers working on nonstationary, heterogeneous, and weakly supervised data streams.