Reinforcement Evolutionary Learning Method for self-learning

10/07/2018
by   Kumarjit Pathak, et al.
0

In statistical modelling the biggest threat is concept drift which makes the model gradually showing deteriorating performance over time. There are state of the art methodologies to detect the impact of concept drift, however general strategy considered to overcome the issue in performance is to rebuild or re-calibrate the model periodically as the variable patterns for the model changes significantly due to market change or consumer behavior change etc. Quantitative research is the most widely spread application of data science in Marketing or financial domain where applicability of state of the art reinforcement learning for auto-learning is less explored paradigm. Reinforcement learning is heavily dependent on having a simulated environment which is majorly available for gaming or online systems, to learn from the live feedback. However, there are some research happened on the area of online advertisement, pricing etc where due to the nature of the online learning environment scope of reinforcement learning is explored. Our proposed solution is a reinforcement learning based, true self-learning algorithm which can adapt to the data change or concept drift and auto learn and self-calibrate for the new patterns of the data solving the problem of concept drift. Keywords - Reinforcement learning, Genetic Algorithm, Q-learning, Classification modelling, CMA-ES, NES, Multi objective optimization, Concept drift, Population stability index, Incremental learning, F1-measure, Predictive Modelling, Self-learning, MCTS, AlphaGo, AlphaZero

READ FULL TEXT

page 6

page 10

research
08/29/2019

An Auto-ML Framework Based on GBDT for Lifelong Learning

Automatic Machine Learning (Auto-ML) has attracted more and more attenti...
research
10/19/2020

Learning Parameter Distributions to Detect Concept Drift in Data Streams

Data distributions in streaming environments are usually not stationary....
research
09/25/2019

Online Semi-Supervised Concept Drift Detection with Density Estimation

Concept drift is formally defined as the change in joint distribution of...
research
10/06/2016

Adaptive Online Sequential ELM for Concept Drift Tackling

A machine learning method needs to adapt to over time changes in the env...
research
01/25/2022

Bilevel Online Deep Learning in Non-stationary Environment

Recent years have witnessed enormous progress of online learning. Howeve...
research
09/27/2018

Queue-based Resampling for Online Class Imbalance Learning

Online class imbalance learning constitutes a new problem and an emergin...
research
07/24/2019

Towards AutoML in the presence of Drift: first results

Research progress in AutoML has lead to state of the art solutions that ...

Please sign up or login with your details

Forgot password? Click here to reset