Improving Machine Learning Performance by Eliminating the Influence of Unclean Data
Regardless of the data source and type (text, digital, photo group, etc.), they are usually unclean data. The term (unclean) means that data contains some bugs and paradoxes that can strongly impact machine learning processes. The nature of the input data of the dataset is the most important reason...
Main Authors: | Murtadha Ressan, Rehab Hassan |
---|---|
Format: | Article |
Language: | English |
Published: |
Unviversity of Technology- Iraq
2022-04-01
|
Series: | Engineering and Technology Journal |
Subjects: | |
Online Access: | https://etj.uotechnology.edu.iq/article_173805_3c6f62ef0aa7d8122ed78f05a7515a5d.pdf |
Similar Items
-
A Memory-Efficient Encoding Method for Processing Mixed-Type Data on Machine Learning
by: Ivan Lopez-Arevalo, et al.
Published: (2020-12-01) -
Improving accuracy of missing data imputation in data mining
by: Nzar A. Ali, et al.
Published: (2017-08-01) -
A missing power data filling method based on improved random forest algorithm
by: Wei Deng, et al.
Published: (2019-12-01) -
Opportunities and challenges in machine learning‐based newborn screening—A systematic literature review
by: Elaine Zaunseder, et al.
Published: (2022-05-01) -
Data Reduction Techniques: A Comparative Study
by: Ahmed AlKarawi, et al.
Published: (2022-08-01)