Book ChapterDOI
Visualizing and Evaluating Complexity of Textual Case Bases
Sutanu Chakraborti,Ulises Cerviño Beresi,Nirmalie Wiratunga,Stewart Massie,Robert Lothian,Deepak Khemani +5 more
- pp 104-119
TLDR
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 and a complexity measure called GAME that exploits regularities in stacked images to evaluate the alignment between problem and solution components of cases are presented.Abstract:
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.read more
Citations
More filters
Proceedings ArticleDOI
Effective Document Labeling with Very Few Seed Words: A Topic Model Approach
TL;DR: A Seed-Guided Topic Model (named STM) is proposed for the dataless text classification task, which assumes that each document is associated with a single category-topic and a mixture of general-topics, and consistently outperforms the state-of-the-art datalESS text classifiers.
Journal ArticleDOI
An integrated feature selection and cluster analysis techniques for case-based reasoning
TL;DR: A hybrid CBR system is proposed by introducing reduction technique in feature selection and cluster analysis in case organization and the results indicate that the research techniques can effectively enhance the performance of theCBR system.
Journal ArticleDOI
Seed-Guided Topic Model for Document Filtering and Classification
TL;DR: This article proposes a seed-guided topic model for the dataless text filtering and classification (named DFC), and conducts a thorough study about the impact of seed words for existing datalESS text classification techniques.
Proceedings ArticleDOI
Topic labeled text classification: a weakly supervised approach
TL;DR: An approach that delivers effectiveness comparable to the state-of-the-art supervised techniques in hard-to-classify domains, with very low overheads in terms of manual knowledge engineering is proposed.
Book ChapterDOI
Evaluation Measures for TCBR Systems
TL;DR: This work proposes three measures that can be used to compare alternate TCBR system configurations, in the absence of class information, to quantify alignment as the degree to which similar problems have similar solutions.
References
More filters
Book
Introduction to Algorithms
TL;DR: The updated new edition of the classic Introduction to Algorithms is intended primarily for use in undergraduate or graduate courses in algorithms or data structures and presents a rich variety of algorithms and covers them in considerable depth while making their design and analysis accessible to all levels of readers.
Journal ArticleDOI
Indexing by Latent Semantic Analysis
TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.
Book ChapterDOI
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
TL;DR: This paper explores the use of Support Vector Machines for learning text classifiers from examples and analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task.