.png){kind=icon}
返回
Optimal-Margin Evolutionary Classifier
.png){kind=icon}
OA
AI
DOI：10.1109/TEVC.2019.2895298.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We introduce a novel approach for discriminative classification using evolutionary algorithms. We first propose an algorithm to optimize the total loss value using a modified 0-1 loss function in a 1-D space for classification. We then extend this algorithm for multidimensional classification using an evolutionary algorithm. The proposed evolutionary algorithm aims to find a hyperplane that best classifies instances while minimizing the classification risk. We test particle swarm optimization, evolutionary strategy (ES), and covariance matrix adaptation ES for optimization purposes. After parameter selection, we compare our results with well-established and state-of-the-art classification algorithms, for both binary and multiclass classification, on 23 benchmark classification problems, with and without noise and outliers. We also compare these methods on a seizure detection task for 12 epileptic patients. Results show that the performance of the proposed algorithm is significantly (Wilcoxon test) better than all other methods in almost all problems tested. We also show that the proposed algorithm is significantly more robust against noise and outliers comparing to other methods. The running time of the algorithm is within a reasonable range for the solution of real-world classification problems.
Keyword:
Optimization
Support vector machines
Particle separators
Evolutionary computation
Training
Logistics
Fasteners
Discriminative classification
evolutionary algorithms (EAs)
supervised learning
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:
12
论文数:
1.9K
被引数:
2.4W
机构
引用论文
THE MEANING AND USE OF THE AREA UNDER A RECEIVER OPERATING CHARACTERISTIC (ROC) CURVE受试者工作特征 (ROC) 曲线下面积的含义和用途.png)
RADIOLOGY
RADIOLOGYIF15.2
PREAI
分享收藏分享收藏
查看更多内容