Dynamic Classification Trees for imprecise data
نویسندگان
چکیده
This paper provides a supervised classification tree-based methodology to deal with Multivalued data, specifically predictors measurements can be provided by a functional distribution or an interval of values. Main literature refers to symbolic data analysis, aiming to extend standard methods such as factorial analysis, clustering, discriminant analysis, etc., to deal with symbolic data tables. One approach is to define a suitable data pre-processing enabling the application of standard methods. A more correct approach is to define suitable methods to deal specifically with unstandard data. In the framework of supervised classification, there are no proposal in literature for supervised classification methods to deal with both standard and multivalued data as well. There are only proposals based on data pre-processing. This paper provides a methodology to grow the so-called Dynamic CLASSification TREE (D-CLASSTREE), upon suitable definition of both a specific splitting criterion and a tree-growing algorithm. A real world case study will be considered to show the advantages of the final output and main issues of the interpretation. A comparative study with older proposals will be also described such to demonstrate the stability and the better accuracy of the D-CLASSTREE.
منابع مشابه
MASTER THESIS by Paul Fink Ensemble methods for classification trees under imprecise probabilities
In this master thesis some properties of bags of imprecise classification trees, as introduced in Abellán and Masegosa (2010), are analysed. In the beginning the statistical background of imprecise classification trees is outlined – starting with an overview on measuring uncertainty within the concept of Dempster–Shafer theory is presented, followed by a discussion of its application in a tree–...
متن کاملVariable Selection Bias in Classification Trees Based on Imprecise Probabilities
Classification trees are a popular statistical tool with multiple applications. Recent advancements of traditional classification trees, such as the approach of classification trees based on imprecise probabilities by Abellán and Moral (2005), effectively address their tendency to overfitting. However, another flaw inherent in traditional classification trees is not eliminated by the imprecise ...
متن کاملVariable Selection in Classification Trees Based on Imprecise Probabilities
Classification trees are a popular statistical tool with multiple applications. Recent advancements of traditional classification trees, such as the approach of classification trees based on imprecise probabilities by Abellán and Moral (2004), effectively address their tendency to overfitting. However, another flaw inherent in traditional classification trees is not eliminated by the imprecise ...
متن کاملPredicting The Type of Malaria Using Classification and Regression Decision Trees
Predicting The Type of Malaria Using Classification and Regression Decision Trees Maryam Ashoori1 *, Fatemeh Hamzavi2 1School of Technical and Engineering, Higher Educational Complex of Saravan, Saravan, Iran 2School of Agriculture, Higher Educational Complex of Saravan, Saravan, Iran Abstract Background: Malaria is an infectious disease infecting 200 - 300 million people annually. Environme...
متن کاملA New Algorithm for Optimization of Fuzzy Decision Tree in Data Mining
Decision-tree algorithms provide one of the most popular methodologies for symbolic knowledge acquisition. The resulting knowledge, a symbolic decision tree along with a simple inference mechanism, has been praised for comprehensibility. The most comprehensible decision trees have been designed for perfect symbolic data. Classical crisp decision trees (DT) are widely applied to classification t...
متن کامل