A comparative study of decision tree ID3 and C4.5

By Badr Hssina, Abdelkarim Merbouha, Hanane Ezzikouri, Mohammed Erritali. 2014

407 citations recorded by the source corpus.

Data mining is the useful tool to discovering the knowledge from large data. Different methods & algorithms are available in data mining. Classification is most common method used for finding the mine rule from the large database. Decision tree method generally used for the Classification, because it is the simple hierarchical structure for the user understanding & decision making. Various data mining algorithms available for classification based on Artificial Neural Network, Nearest Neighbour Rule & Baysen classifiers but decision tree mining is simple one. ID3 and C4.5 algorithms have been introduced by J.R Quinlan which produce reasonable decision trees. The objective of this paper is to present these algorithms. At first we present the classical algorithm that is ID3, then highlights of this study we will discuss in more detail C4.5 this one is a natural extension of the ID3 algorithm. And we will make a comparison between these two algorithms and others algorithms such as C5.0 and CART.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/107b422066a5904b2b0416a9bae194940614efbd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chaitanya Kulkarni
2. Mayur Wankhade
3. A. C. Rao

In Information Systems

1. Chaitanya Kulkarni
2. Mayur Wankhade
3. A. C. Rao

Citations in the corpus

• A survey on sentiment analysis methods, applications, and challenges (2022, methodology): “2020), and additional algorithms like LASSO (Hssina et al. 2014).”