Pierre Parrend

Accurate and interpretable representations of environments with anticipatory learning classifier systems

By Romain Orhand, Anne Jeannin-Girardon, Pierre Parrend, Pierre Collet

2022-01-01

In European conference on genetic programming (part of EvoStar)

Abstract Anticipatory Learning Classifier Systems (ALCS) are rule- based machine learning algorithms that can simultaneously develop a complete representation of their environment and a decision policy based on this representation to solve their learning tasks. This paper intro- duces BEACS (Behavioral Enhanced Anticipatory Classifier System) in order to handle non-deterministic partially observable environments and to allow users to better understand the environmental representations issued by the system. BEACS is an ALCS that enhances and merges Probability-Enhanced Predictions and Behavioral Sequences approaches used in ALCS to handle such environments.

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One-class ant-miner: Selection of majority class rules for binary rule-based classification

By Naser Ghannad, Roland de Guio, Pierre Parrend

2022-01-01

In International conference on artificial evolution (EA-2022)

Abstract In recent years, high-performance models have been introduced based on deep learning; however, these models do not have high interpretability to complement their high efficiency. Rule-based classifiers can be used to obtain explainable artificial intelligence. Rule-based classifiers use a labeled dataset to extract rules that express the relationships between inputs and expected outputs. Although many evolutionary and non-evolutionary algorithms have developed to solve this problem, we hypothesize that rule-based evolutionary algorithms such as the AntMiner family can provide good approximate solutions to problems that cannot be addressed efficiently using other techniques.

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Evaluation of anomaly detection for cybersecurity using inductive node embedding with convolutional graph neural networks

By Amina Abou Rida, Pierre Parrend, Rabih Amhaz

2021-10-01

In Complex network 2021

Abstract In the face of continuous cyberattacks, many scientists have proposed machine learning-based network anomaly detection methods. While deep learning effectively captures unseen patterns of Euclidean data, there is a huge number of applications where data are described in the form of graphs. Graph analysis have improved detecting anomalies in non-Euclidean domains, but it suffered from high computational cost. Graph embeddings have solved this problem by converting each node in the network into low dimensional representation, but it lacks the ability to generalize to unseen nodes.

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