# EILearn: Learning Incrementally Using Previous Knowledge Obtained From an Ensemble of Classifiers

## TL;DR

EILearn introduces an incremental learning algorithm that leverages an ensemble of classifiers and previous knowledge, effectively balancing stability and plasticity to improve performance over existing methods.

## Contribution

The paper presents a novel ensemble-based incremental learning approach that retains relevant past knowledge and dynamically manages classifiers to enhance learning efficiency.

## Key findings

- Outperforms existing incremental learning methods
- Effectively balances stability and plasticity
- Maintains relevant knowledge from previous phases

## Abstract

We propose an algorithm for incremental learning of classifiers. The proposed method enables an ensemble of classifiers to learn incrementally by accommodating new training data. We use an effective mechanism to overcome the stability-plasticity dilemma. In incremental learning, the general convention is to use only the knowledge acquired in the previous phase but not the previously seen data. We follow this convention by retaining the previously acquired knowledge which is relevant and using it along with the current data. The performance of each classifier is monitored to eliminate the poorly performing classifiers in the subsequent phases. Experimental results show that the proposed approach outperforms the existing incremental learning approaches.