Automated Problem Identification: Regression vs Classification via Evolutionary Deep Networks

07/03/2017
by   Emmanuel Dufourq, et al.
0

Regression or classification? This is perhaps the most basic question faced when tackling a new supervised learning problem. We present an Evolutionary Deep Learning (EDL) algorithm that automatically solves this by identifying the question type with high accuracy, along with a proposed deep architecture. Typically, a significant amount of human insight and preparation is required prior to executing machine learning algorithms. For example, when creating deep neural networks, the number of parameters must be selected in advance and furthermore, a lot of these choices are made based upon pre-existing knowledge of the data such as the use of a categorical cross entropy loss function. Humans are able to study a dataset and decide whether it represents a classification or a regression problem, and consequently make decisions which will be applied to the execution of the neural network. We propose the Automated Problem Identification (API) algorithm, which uses an evolutionary algorithm interface to TensorFlow to manipulate a deep neural network to decide if a dataset represents a classification or a regression problem. We test API on 16 different classification, regression and sentiment analysis datasets with up to 10,000 features and up to 17,000 unique target values. API achieves an average accuracy of 96.3% in identifying the problem type without hardcoding any insights about the general characteristics of regression or classification problems. For example, API successfully identifies classification problems even with 1000 target values. Furthermore, the algorithm recommends which loss function to use and also recommends a neural network architecture. Our work is therefore a step towards fully automated machine learning.

READ FULL TEXT
research
06/25/2022

Binary and Multinomial Classification through Evolutionary Symbolic Regression

We present three evolutionary symbolic regression-based classification a...
research
06/04/2021

A novel multi-scale loss function for classification problems in machine learning

We introduce two-scale loss functions for use in various gradient descen...
research
06/22/2018

Combination of Domain Knowledge and Deep Learning for Sentiment Analysis

The emerging technique of deep learning has been widely applied in many ...
research
09/26/2017

EDEN: Evolutionary Deep Networks for Efficient Machine Learning

Deep neural networks continue to show improved performance with increasi...
research
11/03/2019

Generalized Learning with Rejection for Classification and Regression Problems

Learning with rejection (LWR) allows development of machine learning sys...
research
08/21/2017

Deep vs. Diverse Architectures for Classification Problems

This study compares various superlearner and deep learning architectures...
research
02/11/2020

Neuroevolution of Neural Network Architectures Using CoDeepNEAT and Keras

Machine learning is a huge field of study in computer science and statis...

Please sign up or login with your details

Forgot password? Click here to reset