| Autor: |
Shriram Hegde, Rakshith Gowda G. S, Myna A. N, Swaroop L. R, Sourabh U |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
2019 1st International Conference on Advances in Information Technology (ICAIT). |
| DOI: |
10.1109/icait47043.2019.8987346 |
| Popis: |
Gender identification using the name of a person, specifically for Kannada names, is a challenging task. We present a classification approach for gender prediction of Kannada names represented in Kannada Unicode. We have determined various features derived from extensive morphological analysis of the names in Kannada. Some of the features identified are indigenous to Kannada Language. In this work we have developed three different classification models using Support Vector Machine (SVM), Random Forest and Naive Bayes machine learning algorithms. Our system reports a top accuracy of 90.1%, F 1 score of 90.1% for male names and 90.0% for female names. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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