A novel supervised learning algorithm and its use for Spam Detection in Social Bookmarking Systems
Abstract
A novel fast and accurate supervised learning algorithm isproposed as a general text classification algorithm for linearly separateddata. The strategy of the algorithm takes advantage of the training errorsto successively refine an initial classifier. Experimental evaluation of theproposed algorithm on standard text collections, show that results com-pared favorably to those from state of the art algorithms such as SVMs. Experiments conducted on the datasets provided in the framework of theECDL/PKDD 2008 Challenge for Spam Detection in Social Bookmark-ing Systems, demonstrate the effectiveness of the proposed algorithm.
Type
Publication
In Winner of ECML PKDD Discovery Challenge 2008 (DC08), task 1: Spam Detection in Social Bookmarking Systems, Adwerp, Belgium, 2008
Authors
Senior AI/ML and genomics practitioner
Senior AI/ML and genomics practitioner with ~15 years building open-source, production-grade tools for large-scale biological data. Maintainer of multiple Bioconductor packages (fastreeR, metabinR, jvecfor), and author of agentic, LLM-driven tooling that runs reproducible bioinformatics workflows from natural-language requests.
Broad multi-omics background spanning genome assembly and annotation, population genomics, large-scale NGS and functional-genomics analysis, and metagenomics, backed by reproducible HPC software and end-to-end program leadership. Currently focused on bringing modern AI — embeddings, deep learning, and LLM-based agents — to making complex omics datasets faster and easier to interrogate.