Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results | Citedby Results | Evolutionary Computation | MIT Press

Skip to Main Content

Skip Nav Destination

Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results

Evolutionary Computation (2016) 24 (3): 385–409.

This article has been cited by the following articles in journals that are participating in Crossref Cited-by Linking.

Classification Algorithm Using Branches Importance

Youness Manzali
Mohamed Chahhou
Mohammed El Mohajir

Neural Processing Letters (2022) 54 (2): 891.

Network Traffic Statistics Method for Resource-Constrained Industrial Project Group Scheduling under Big Data

Zongjie Huo
Wei Zhu
Pei Pei
Wenqing Wu

Wireless Communications and Mobile Computing (2021) 2021: 1.

Notions of explainability and evaluation approaches for explainable artificial intelligence

Giulia Vilone
Luca Longo

Information Fusion (2021) 76: 89.

Classification of Explainable Artificial Intelligence Methods through Their Output Formats

Giulia Vilone
Luca Longo

Machine Learning and Knowledge Extraction (2021) 3 (3): 615.

Short-term wind speed forecasting based on improved ant colony algorithm for LSSVM

Yanan Li
Peng Yang
Huajun Wang

Cluster Computing (2019) 22 (S5): 11575.

Research on tourism individualized route management based on intelligent optimization algorithm

Xiaojuan Yuan
Chunsheng Shi

Journal of Computational Methods in Sciences and Engineering (2019) 19 (4): 1065.

A cost-sensitive meta-learning classifier: SPFCNN-Miner

Linchang Zhao
Zhaowei Shang
Anyong Qin
Taiping Zhang
Ling Zhao
Yu Wei
Yuan Yan Tang

Future Generation Computer Systems (2019) 100: 1031.

Application of artificial intelligence algorithms in image processing

Xin Zhang
Wang Dahu

Journal of Visual Communication and Image Representation (2019) 61: 42.