So handling incomplete data is important and necessary for building a high quality classification model. In this paper a new decision tree is proposed to solve ...
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Abstract—Classification is a very important research topic in knowledge discovery and machine learning. Decision-tree is one of the well-known data mining ...
This paper proposes a new decision tree to solve the incomplete data classification problem and it has a very good performance and solves two other ...
Nov 10, 2021 · It sounds like you're Dealing with Missing Data. Missing data can be handled in two ways: (1) delete values (rows or columns) or (2) perform ...
Aug 15, 2018 · The main advantage of this approach is that it can classify new incomplete instances without requiring any imputation.
Apr 13, 2016 · 1 Answer 1 ... 1) For the features that were used to build the classifier, were there missing values in your training set as well? If so, you must ...
Missing: Incomplete | Show results with:Incomplete
The new classifier delivers classifications that are reliable even in the presence of small sample sizes and missing values. Extensive empirical evaluations ...
One of the most popular approaches to solving classification with incomplete data is to use a classifier which can directly classify incomplete data. For ex-.
Nov 25, 2020 · BART.m outperforms common models for classification with incomplete data, according to accuracy and computational time. Based on the revealed ...
Jun 4, 2024 · In this paper, we propose a novel evidential classification approach to address such a problem based on the Dempster-Shafer theory. First, ...