A customized classification algorithm for credit card fraud detection
This paper presents Fraud-BNC, a customized Bayesian Network Classifier (BNC) algorithm
for a real credit card fraud detection problem. The task of creating Fraud-BNC was …
for a real credit card fraud detection problem. The task of creating Fraud-BNC was …
Learning neural network structures with ant colony algorithms
KM Salama, AM Abdelbar - Swarm Intelligence, 2015 - Springer
Ant colony optimization (ACO) has been successfully applied to classification, where the aim
is to build a model that captures the relationships between the input attributes and the target …
is to build a model that captures the relationships between the input attributes and the target …
A novel ant colony algorithm for building neural network topologies
K Salama, AM Abdelbar - International Conference on Swarm Intelligence, 2014 - Springer
A re-occurring challenge in applying feed-forward neural networks to a new dataset is the
need to manually tune the neural network topology. If one's attention is restricted to fully …
need to manually tune the neural network topology. If one's attention is restricted to fully …
Parameter self-adaptation in an ant colony algorithm for continuous optimization
AM Abdelbar, KM Salama - IEEE Access, 2019 - ieeexplore.ieee.org
ACO R is a well-established ant colony optimization algorithm for continuous-domain
optimization. We present an approach for the dynamic adaptation of the ACOR algorithm's …
optimization. We present an approach for the dynamic adaptation of the ACOR algorithm's …
Classification with cluster-based Bayesian multi-nets using Ant Colony Optimisation
KM Salama, AA Freitas - Swarm and Evolutionary Computation, 2014 - Elsevier
Bayesian multi-net (BMN) classifiers consist of several local models, one for each data
subset, to model asymmetric, more consistent dependency relationships among variables in …
subset, to model asymmetric, more consistent dependency relationships among variables in …
Learning multi-tree classification models with ant colony optimization
Ant Colony Optimization (ACO) is a meta-heuristic for solving combinatorial optimization
problems, inspired by the behaviour of biological ant colonies. One of the successful …
problems, inspired by the behaviour of biological ant colonies. One of the successful …
Instance-based classification with ant colony optimization
Instance-based learning (IBL) methods predict the class label of a new instance based
directly on the distance between the new unlabeled instance and each labeled instance in …
directly on the distance between the new unlabeled instance and each labeled instance in …
A hyper-heuristic evolutionary algorithm for learning bayesian network classifiers
Hyper-heuristic evolutionary algorithms (HHEA) are successful methods for selecting and
building new heuristics or algorithms to solve optimization or machine learning problems …
building new heuristics or algorithms to solve optimization or machine learning problems …
Investigating evaluation measures in ant colony algorithms for learning decision tree classifiers
KM Salama, AM Abdelbar… - 2015 IEEE symposium …, 2015 - ieeexplore.ieee.org
Classification is a data mining task where the goal is to build, from labeled cases, a model
that can be used to predict the class of unlabeled cases. Ant-Tree-Miner is a decision tree …
that can be used to predict the class of unlabeled cases. Ant-Tree-Miner is a decision tree …
[图书][B] Swarm intelligence
These proceedings contain the papers presented at ANTS 2010, the 7th International
Conference on Swarm Intelligence, organized by IRIDIA, CoDE, Université Libre de …
Conference on Swarm Intelligence, organized by IRIDIA, CoDE, Université Libre de …