Abstract
Decision trees are one of the most popular classifiers used
in a wide range of real-world problems. Thus, it is very important to
achieve higher prediction accuracy for decision trees. Most of the well-known
decision tree induction algorithms used in practice are based on
greedy approaches and hence do not consider conditional dependencies
among the attributes. As a result, they may generate sub-optimal solutions.
In literature, often genetic programming-based (a complex variant
of genetic algorithm) decision tree induction algorithms have been proposed
to eliminate some of the problems of greedy approaches. However,
none of the algorithms proposed so far can effectively address conditional
dependencies among the attributes. In this paper, we propose a new,
easy-to-implement genetic algorithm-based decision tree induction technique
which is more likely to ascertain conditional dependencies among
the attributes. An elaborate experimentation is conducted on thirty well-known data sets from the UCI Machine Learning Repository in order to
validate the effectiveness of the proposed technique.
in a wide range of real-world problems. Thus, it is very important to
achieve higher prediction accuracy for decision trees. Most of the well-known
decision tree induction algorithms used in practice are based on
greedy approaches and hence do not consider conditional dependencies
among the attributes. As a result, they may generate sub-optimal solutions.
In literature, often genetic programming-based (a complex variant
of genetic algorithm) decision tree induction algorithms have been proposed
to eliminate some of the problems of greedy approaches. However,
none of the algorithms proposed so far can effectively address conditional
dependencies among the attributes. In this paper, we propose a new,
easy-to-implement genetic algorithm-based decision tree induction technique
which is more likely to ascertain conditional dependencies among
the attributes. An elaborate experimentation is conducted on thirty well-known data sets from the UCI Machine Learning Repository in order to
validate the effectiveness of the proposed technique.
| Original language | English |
|---|---|
| Title of host publication | Advanced Data Mining and Applications |
| Editors | Guojun Gan, Bohan Li, Xue Li, Shuliang Wang |
| Publisher | LNAI |
| Chapter | 1 |
| Pages | 80 - 94 |
| Number of pages | 15 |
| Volume | 11323 |
| ISBN (Electronic) | 9783030050900 |
| ISBN (Print) | 9783030050894 |
| DOIs | |
| Publication status | Published - Nov 2018 |
| Event | 14th International Conference on Advanced Data Mining and Applications : ADMA 2018 - Marriott Nanjing South Hotel , Nanjing, China Duration: 16 Nov 2018 → 18 Nov 2018 https://link.springer.com/book/10.1007/978-3-030-05090-0 (Springer link to proceedings) http://adma2018.nuaa.edu.cn/main.htm (Conference website) https://link-springer-com.ezproxy.csu.edu.au/content/pdf/bfm%3A978-3-030-05090-0%2F1.pdf (Proceedings front matter) |
Publication series
| Name | Lecture Notes in Artificial Intelligence |
|---|
Conference
| Conference | 14th International Conference on Advanced Data Mining and Applications |
|---|---|
| Country/Territory | China |
| City | Nanjing |
| Period | 16/11/18 → 18/11/18 |
| Other | The conference aims at bringing together the experts on data mining from around the world, and providing a leading international forum for the dissemination of original research findings in data mining, spanning applications, algorithms, software and systems, as well as different applied disciplines with potential in data mining, such as smartphone and social network mining, bio-medical science and green computing. ADMA 2018 will promote the same close interaction and collaboration among practitioners and researchers. Published papers will go through a full peer review process. |
| Internet address |
|