Name
Abstract | ||
|---|---|---|
Data preprocessing or cleansing is one of the biggest hurdles in industry for developing successful machine learning applications. The process of data cleansing includes data imputation, feature normalization & selection, dimensionality reduction, and data balancing applications. Currently such preprocessing is manual. One approach for automating this process is meta-learning. In this paper, we experiment with state of the art meta-learning methodologies and identify the inadequacies and research challenges for solving such a problem. |
Year | Venue | Field |
|---|---|---|
2017 | THIRTY-FIRST AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE | Data cleansing,Computer science,Artificial intelligence,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
3 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ian M. Gemp | 1 | 16 | 6.37 |
| Georgios Theocharous | 2 | 140 | 16.65 |
| Mohammad Ghavamzadeh | 3 | 814 | 67.73 |