.png){kind=icon}
返回
Designing efficient discriminant functions for multi-category classification using evolutionary methods
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2015.08.093.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we propose two approaches to obtain accurate classifiers for dealing with multi-category classification problem. Our work is based on one-vs-all strategy where we try to decrease conflicting situations. In the first phase of both approaches we employ Genetic Programming to find populations of the best discriminant functions (one population for each class). In addition to traditional function set, like {+, -, *, /}, we utilize other special functions in our binary trees. We also use both negative and positive constants in the terminal nodes of the trees. In the second phase, we employ Ant Colony in our first approach, called GP-Ant, and Genetic Algorithm in the second one, called GP-GA, to find the best combination of discriminant functions found in the previous phase. We also provide a special modification box to modify the decision of our final integrated classifiers, when conflicting situations happen. To cope with conflicting situations, we also utilize an appropriate fitness function in the second phase. We compare our works with both state of the art and basic multi-category classification methods on eight well-known publicly available data sets. Our experimental results show that our methods are statistically significantly better than all the Other classification methods used. (C) 2015 Elsevier B.V. All rights reserved.
Keyword:
Multi-category classification
Ant colony system
Genetic programming
Discriminant functions
One-vs-all strategy
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
机构
引用论文
分享收藏Modifying genetic programming for artificial neural network development for data mining.png)
SOFT COMPUTING
SOFT COMPUTINGIF2.5
PREAI
分享收藏
A multi-objective evolutionary algorithm-based ensemble optimizer for feature selection and classification with neural network models基于多目标进化算法的神经网络特征选择和分类集成优化器NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏
查看更多内容