Sutanu Chakraborti

Textual-case based reasoning (TCBR) systems where the problem and solution are in free text form are hard to evaluate. In the absence of class information, domain experts are needed to evaluate solution quality, and provide relevance information. This approach is costly and time consuming. We propose three measures that can be used to compare alternate TCBR system configurations, in the absence of class information. The main idea is to quantify alignment as the degree to which similar problems have similar solutions. Two local measures capture this information by analysing similarity between problem and solution neighbourhoods at different levels of granularity, whilst a global measure achieves the same by analyzing similarity between problem and solution clusters. We determine the suitability of the proposed measures by studying their correlation with classifier accuracy on a health and safety incident reporting task. Strong correlation is observed with all three approaches with local measures being slightly superior over the global one. © Springer-Verlag Berlin Heidelberg 2008.

In TCBR, complexity refers to the extent to which similar problems have similar solutions. Casebase complexity measures proposed are based on the premise that a casebase is simple if similar problems have similar solutions. We observe, however, that such measures are vulnerable to choice of solution side representations, and hence may not be meaningful unless similarities between solution components of cases are shown to corroborate with human judgements. In this paper, we redefine the goal of complexity measurements and explore issues in estimating solution side similarities. A second limitation of earlier approaches is that they critically rely on the choice of one or more parameters. We present two parameter-free complexity measures, and propose a visualization scheme for casebase maintenance. Evaluation over diverse textual casebases show their superiority over earlier measures. © 2009 Springer Berlin Heidelberg.

This paper deals with two relatively less well studied problems in Textual CBR, namely visualizing and evaluating complexity of textual case bases. The first is useful in case base maintenance, the second in making informed choices regarding case base representation and tuning of parameters for the TCBR system, and also for explaining the behaviour of different retrieval/classification techniques over diverse case bases. We present an approach to visualize textual case bases by "stacking" similar cases and features close to each other in an image derived from the case-feature matrix. We propose a complexity measure called GAME that exploits regularities in stacked images to evaluate the alignment between problem and solution components of cases. GAME class , a counterpart of GAME in classification domains, shows a strong correspondence with accuracies reported by standard classifiers over classification tasks of varying complexity. © Springer-Verlag Berlin Heidelberg 2008.