Decision Tree and Naïve Bayes Algorithm for Classification and Generation of Actionable Knowledge for Direct Marketing
By Masud Karim, Rashedur Mohammad Rahman. 2013
140 citations recorded by the source corpus.
Many companies like credit card, insurance, bank, retail industry require direct marketing. Data mining can help those institutes to set marketing goal. Data mining techniques have good prospects in their target audiences and improve the likelihood of response. In this work we have investigated two data mining techniques: the Naive Bayes and the C4.5 decision tree algorithms. The goal of this work is to predict whether a client will subscribe a term deposit. We also made comparative study of performance of those two algorithms. Publicly available UCI data is used to train and test the performance of the algorithms. Besides, we extract actionable knowledge from decision tree that focuses to take interesting and important decision in business area.
Subfields: Artificial Intelligence, Information Systems, Marketing
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
- Bernhard Schölkopf
- Amir-Hossein Karimi
- Isabel Valera
- Gilles Barthe
- Yixin Chen
- Zhicheng Cui
- Dominik Janzing
- Arthur Gretton
In Information Systems
- Yixin Chen
- Isabel Valera
- Gilles Barthe
- Amir-Hossein Karimi
- Bernhard Schölkopf
- Yujie He
- Zhicheng Cui
- Aynaz Taheri
In Marketing
Citations in the corpus
- A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, methodology): “Ac-tionable knowledge extraction is employed in data mining to suggest “behaviors which render a state of an instance into a preferred state” [57] according to a classifier [36–38, 90, 186].”
- Optimal Action Extraction for Random Forests and Boosted Trees (2015, background): “Techniques have also been proposed to postprocess decision trees to extract actionable knowledge [31, 19, 30, 12].”