.png){kind=icon}
.png){kind=icon}
Return
High-dimensional QSAR/QSPR classification modeling based on improving pigeon optimization algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.chemolab.2020.104170.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
High-dimensionality is one of the major problems which affect the quality of the quantitative structure-activity (property) relationship (QSAR/QSPR) classification methods in chemometrics. Applying variable selection is essential to improve the performance of the classification task. Variable selection is well-known as an NP-hard optimization problem. Various evolutionary algorithms are dedicated to solving this problem in the literature. Recently, a pigeon optimization algorithm was proposed, which has been successfully applied to solve various continuous optimization problems. In this paper, a new time-varying transfer function is proposed to improve the exploration and exploitation capability of the binary pigeon optimization algorithm in selecting the most relevant descriptors (variables) in QSAR/QSPR classification models with high classification accuracy and short computing time. Based on seven benchmark biopharmaceutical datasets, the experimental results reveal the capability of the proposed time-varying transfer function to achieve high classification accuracy with minimizing the number of selected descriptors and reducing the computational time.
Keywords:
QSAR
Pigeon optimization algorithm
Evolutionary algorithm
Transfer function
Descriptors selection
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.8
Papers:
4.6K
Citations:
1.2W
View more organizations