Dropping forward-backward algorithms for feature selection

10/17/2019
by   Thu Nguyen, et al.
0

In this era of big data, feature selection techniques, which have long been proven to simplify the model, makes the model more comprehensible, speed up the process of learning, have become more and more important. Among many developed methods, forward, backward and stepwise feature selection regression remained widely used due to their simplicity and efficiency. However, they are not sufficient enough when it comes to large datasets. In this paper, we analyze the issues associated with those approaches and introduce a novel algorithm that may boost the speed up to 23.11 good performance compared to stepwise regression in terms of error sum of squares and number of selected features.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/06/2022

Synthetic Data for Feature Selection

Feature selection is an important and active field of research in machin...
research
08/23/2017

Massively-Parallel Feature Selection for Big Data

We present the Parallel, Forward-Backward with Pruning (PFBP) algorithm ...
research
07/26/2023

Using Markov Boundary Approach for Interpretable and Generalizable Feature Selection

Predictive power and generalizability of models depend on the quality of...
research
03/03/2022

Parallel feature selection based on the trace ratio criterion

The growth of data today poses a challenge in management and inference. ...
research
11/27/2014

Features in Concert: Discriminative Feature Selection meets Unsupervised Clustering

Feature selection is an essential problem in computer vision, important ...
research
07/21/2020

ADAGES: adaptive aggregation with stability for distributed feature selection

In this era of "big" data, not only the large amount of data keeps motiv...
research
12/01/2015

Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision

Feature selection is essential for effective visual recognition. We prop...

Please sign up or login with your details

Forgot password? Click here to reset