The volume Advances at the frontier of Learning Classifier Systems is already in Springer hands for the final stages of editing and printing. The volume is going to be printed as Springer’s LNCS 4399 volume. You can find the tentative table of contents here.
GECCO 2007 is approaching. The deadline for paper submission is January 17. You can find more instructions on how to format and submit the papers on the GECCO 2007 papers’ page.
by Xavier Llorà, Anusha Priya, and Rohit Bhargava (2006). To appear in the Special Issue on Learning Classifier Systems of the Natural Computing Journal. Also as IlliGAL TR No. 2006027. Link to the PDF. Abstract Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men, and it is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopy of stained tissue is now able to provide very large data sets describing the chemical properties of the cells forming the prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), we seek to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learn tissue type classification—a key step on the diagnosis process. As we will show, genetics-based machine learning (GBML) can be used to approach such a problem. However, there is an urge for efficient and scalable implementations that enable to process such very large data sets. This paper proposes and validates and efficient GBML technique—NAX—based on an incremental genetics-based rule learner that exploits massive parallelisms—via the message passing interface (MPI)—and efficient rule-matching using hardware-implemented operations. Results show the competence of NAX solving the prostate tissue type prediction and how such and efficient implementation makes it a very powerful tool for biomedical image processing. ...
by Noriko Imafuji Yasui, Xavier Llorà, and David E. Goldberg (2006). Illinois Technical Report No. 2006025. Link to the PDF. Abstract In this paper, we propose some methodologies for delineating topic and discussant transitions in online collaborative environments, more precisely, focus group discussions for product conceptualization. First, we propose KEE (Key Elements Extraction) algorithm, an algorithm for simultaneously finding key terms and key persons in a discussion. Based on KEE algorithm, we propose approaches for analyzing two important factors of discussions: discussion dynamics and emerging social networks. Examining our approaches using actual network-based discussion data generated by real focus groups in a marketing environment, we report interesting results that demonstrate how our approaches could effectively discover knowledge in the discussions. ...
by Xavier Llorà, Noriko Imafuji Yasui, David E. Goldberg (2006) Abstract This paper proposes AKEE (Adaptable Key Elements Extraction) algorithm for web-log (blog, for short) mining. AKEE enables us to identify significant information in various blog components (e.g., a blog post, a series of blog posts, a set of series of blog posts, etc.). Illinois Technical Report No. 2006024. Link to the PDF.