# Publications archive

Welcome to my archive of publications. Click on the title of a publication to view a more detailed description and download the publications. In some cases, related material such as posters and presentations are available for download too.

You can view my Google Scholar citations page for an up-to-date citation count, $$h$$-index, etc. I'm also listed on DBLP, twice in Microsoft Academic Search (here and here) and the ACM digital library.

## Inductive Logic Programming and Machine Learning

Inductive logic programming was the focus of my work at Bristol University but I did some related work in the more general field of machine learning. Inductive logic programming is a subfield of machine learning based on logic programming. Examples and background knowledge and the hypotheses generated from them are all expressed in a logic programming language like Prolog. The goal of an ILP system is to produce a logic program which entails all the positive examples and none of the negative examples.

The papers listed below are a mixture of those on the topic of inductive logic programming and those on other machine learning topics.

• New: Tim Kovacs, Simon Rawles, Larry Bull, Masaya Nakata (中田雅也), and Keiki Takadama (髙玉圭樹). XCS-DH: Minimal default hierarchies in XCS. In IEEE Congress on Evolutionary Computation (IEEE CEC 2016) Special Session on New Directions in Evolutionary Machine Learning, 2016.
• Simon Rawles. Object-oriented Data Mining. PhD thesis, Department of Computer Science, University of Bristol, July 2007.
• Simon Rawles and Peter A. Flach. Neighbourhoods of examples for detecting logical redundancy. In Late-breaking Papers from the 15th International Conference on Inductive Logic Programming (ILP 2005), pages 47–52, July 2005. Presented.
• Annalisa Appice, Michelangelo Ceci, Simon Rawles, and Peter A. Flach. Redundant feature elimination for multi-class problems. In The 21st International Conference on Machine Learning (ICML 2004), pages 33–40, 2004. Presented.
• Mark-A. Krogel, Simon Rawles, Filip Železný, Peter A. Flach, Nada Lavrač, and Stefan Wrobel. Comparative evaluation of approaches to propositionalization. In Proceedings of the 13th International Conference on Inductive Logic Programming (ILP 2003), pages 197–214, 2003. Co-presented.
• Lenka Nováková, Jiří Kléma, Michal Jakob, Simon Rawles, and Olga Štěpánková. Trend analysis and risk identification. In Discovery Challenge Workshop, ECML 2003/PKDD 2003, pages 95–107, 2003.
• Peter A. Flach, Hendrik Blockeel, Thomas Gärtner, Marko Grobelnik, Branko Kavšek, Martin Kejkula, Darek Krzywania, Nada Lavrač, Peter Ljubič, Dunja Mladenić, Steve Moyle, Stefan Raeymaekers, Jan Rauch, Simon Rawles, Rita Ribeiro, Gert Sclep, Jan Struyf, Ljupčo Todorovski, Luis Torgo, Dietrich Wettschereck and Shaomin Wu (吴少敏). In Dunja Mladenić, Nada Lavrač, Marko Bohanec and Steve Moyle, editors, Data Mining and Decision Support: Integration and Collaboration. On the road to knowledge: mining 21 years of UK traffic accident reports, chapter 11, pages 143–155. Kluwer Academic Publishers, January 2003.
• Simon Rawles. Symbolic Processing in Neural Networks and its Application to Region Classification. MSc dissertation, Department of Computer Science, University of Bristol, September 2000.

## Computer-Assisted Assessment

Computer-assisted assessement was one of the areas I worked in at the University of Warwick. This involves the study of the use of computers within the assessment process, usually with the aim of (partially) automating it. Of interest was the use of computer software to self-assess, or for an instructor to perform formative, diagnostic and summative testing.

I am putting more and more of these publications online as time goes on and I dig documents out to put up here.