<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Takieddine Seddik, Mohamed</style></author><author><style face="normal" font="default" size="100%">Ouahab KADRI</style></author><author><style face="normal" font="default" size="100%">Abdessemed, Mohamed Rida</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Imputation as Service Using Support Vector Regression: Application to a Photovoltaic System in Algeria</style></title><secondary-title><style face="normal" font="default" size="100%">1st National Conference of Materials sciences And Engineering,(MSE'22)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://hal.archives-ouvertes.fr/hal-03815846/document</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper aims to test the most common imputation methods' effectiveness and choose the most appropriate methods for our data model. In the experimental study, we applied imputation to missing data using the imputation methods: fFill, bFfil, Drop, and Support Vector Regression (SVR). An easy and practical means of comparison is used to evaluate the effectiveness of imputation methods. Therefore, the classification quality criterion is used, and column reference graphs are used because they have a statistically significant relationship. The SVR imputation method was very reliable, and it helped us make a reasonable classification.</style></abstract></record></records></xml>