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.

Sutanu Chakraborti

Department of Computer Science and Engineering

Deepak Khemani

Chlorine compounds

Game theory

Case based

CASE BASES

CASE-BASE MAINTENANCE

Complexity measures

European

FEATURE MATRIX

SIMILAR CASES

SOLUTION COMPONENTS

STACKED IMAGES

Case based reasoning

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Visualizing and Evaluating Complexity of Textual Case Bases

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Search and recommender systems benefit from effective integration of two different kinds of knowledge. The first is introspective knowledge, typically available in feature-theoretic representations of objects. The second is external knowledge, which could be obtained from how users rate (or annotate) items, or collaborate over a social network. This paper presents a spreading activation model that is aimed at a principled integration of these two sources of knowledge. In order to empirically evaluate our approach, we restrict the scope to text classification tasks, where we use the category knowledge of the labeled set of examples as an external knowledge source. Our experiments show a significantly improved classification effectiveness on hard datasets, where feature value representations, on their own, are inadequate in discriminating between classes. © Springer International Publishing Switzerland 2015.

Spreading activation way of knowledge integration

Supervised text classifiers require extensive human expertise and labeling efforts. In this paper, we propose a weakly supervised text classification algorithm based on the labeling of Latent Dirichlet Allocation (LDA) topics. Our algorithm is based on the generative property of LDA. In our algorithm, we ask an annotator to assign one or more class labels to each topic, based on its most probable words. We classify a document based on its posterior topic proportions and the class labels of the topics. We also enhance our approach by incorporating domain knowledge in the form of labeled words. We evaluate our approach on four real world text classification datasets. The results show that our approach is more accurate in comparison to semi-supervised techniques from previous work. A central contribution of this work is an approach that delivers effectiveness comparable to the state-of-the-art supervised techniques in hard-toclassify domains, with very low overheads in terms of manual knowledge engineering. Copyright 2014 ACM.

SIGIR 2014 - Proceedings of the 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

Topic labeled text classification: A weakly supervised approach

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.

Evaluation measures for TCBR systems

Visualizing and evaluating complexity of textual case bases

Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN	03029743
Open Access	No