Interactive Classification Using a Granule Network

Interactive Classification Using a Granule Network

Yan Zhao (University of Regina, Canada) and Yiyu Yao (University of Regina, Canada)
Copyright: © 2009 |Pages: 11
DOI: 10.4018/978-1-60566-170-4.ch016
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Abstract

Classification is one of the main tasks in machine learning, data mining, and pattern recognition. Compared with the extensively studied automation approaches, the interactive approaches, centered on human users, are less explored. This chapter studies interactive classification at 3 levels. At the philosophical level, the motivations and a process-based framework of interactive classification are proposed. At the technical level, a granular computing model is suggested for re-examining not only existing classification problems, but also interactive classification problems. At the application level, an interactive classification system (ICS), using a granule network as the search space, is introduced. ICS allows multi-strategies for granule tree construction, and enhances the understanding and interpretation of the classification process. Interactive classification is complementary to the existing classification methods.
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Introduction

Human cognitive activities rely on classification to organize the vast number of known matter, plants, animals and events into categories that can be named, remembered, and discussed. The problems of human-based classification are that, for very large and separate datasets, it is difficult for people to be aware, to extract, to memorize, to search and to retrieve classification patterns, in addition to interpreting and evaluating classification results that are constantly changing, and then making recommendations or predictions in the face of inconsistent and incomplete data.

Computers perform classification by revealing the internal structures of data according to programmed algorithms. They maintain precise operations under a heavy information load and preserve steady performance. A typical automatic classification approach is batch processing, where all the input is prepared before the program runs. The problems of automatic classification are that the systems often do not allow users, or limit users’ ability, to contact and participate in the discovery process. A fixed algorithm may not satisfy the diverse requirements of users; a user often cannot relate to the answers, and is left wondering about the meaning and value of the so-called discovered knowledge.

In this chapter, we propose a framework of human-machine interactive classification. Although human-machine interaction has been emphasized for many disciplines, such as information retrieval and pattern recognition, it has received some, though not yet enough, attention in the domain of data mining (Ankerst et al., 1999, Brachmann & Anand, 1996, Han, Hu & Cercone, 2003, Zhao & Yao, 2006). The fundamental idea of interactive classification is: on one hand, computers can help users to carry out description, prediction and explanation activities efficiently; on the other hand, human insights, judgements and preferences can effectively interfere with method selection, application and adjustment, thus improving the existing methods and generating new methods. Interactive classification uses the advantages of both a computer system and a human user. A foundation of human-computer interaction in data mining may be provided by cognitive informatics (Wang, 2002, 2007a, 2007b; Wang & Kinsner, 2006; Wang et al., 2006). As Wang suggests that, for cognitive informatics, relations and connections of neurons represent information and knowledge in the human brain might be more important than the neurons. Following the same way of thinking, we believe that interactive data mining is sensitive to capacities and needs of humans and machines. A critical issue is not how intelligent the user is, or how efficient the algorithm is, but how well these two parts can be connected and communicated, stimulated and improved.

More specifically, interactive classification systems allow users to suggest preferred classifiers and knowledge structures, and use machines to assist calculation and analysis during the discovery process. A user can freely explore the dataset according to his/her preference and priority, ensuring that each classification stage and the corresponding result are all understandable and comprehensible. The constructed classifier is not necessarily efficient when compared with most of the automatic classifiers. However, it is close to human thinking by its very nature. The evaluation of an interactive classification, involving the understandability and applicability of the final classification results, relies heavily on the interaction between the computer and the human user, not just on one single factor.

In the rest of this chapter, we discuss the interactive classification at three levels: philosophical level, technical level and application level. At the philosophical level (Section 2), we discuss the motivation of interactive classification and present the process-based framework. At the technical level (Section 3), we apply granular computing as the methodology for examining the search space and complexity issues of interactive classification. At the application level (Section 4), an interactive classification implementation based on a granule network is introduced. The main results demonstrate the usefulness of the proposed approach. The conclusion is in Section 5.

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Table of Contents
Acknowledgment
Yingxu Wang
Chapter 1
Yingxu Wang
Cognitive Informatics (CI) is a transdisciplinary enquiry of the internal information processing mechanisms and processes of the brain and natural... Sample PDF
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Chapter 2
Withold Kinsner
This chapter provides a review of Shannon and other entropy measures in evaluating the quality of materials used in perception, cognition, and... Sample PDF
Is Entropy Suitable to Characterize Data and Signals for Cognitive Informatics?
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Chapter 3
Ismael Rodríguez, Manuel Núñez, Fernando Rubio
Finite State Machines (FSM) are formalisms that have been used for decades to describe the behavior of systems. They can also provide an intelligent... Sample PDF
Cognitive Processes by using Finite State Machines
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Chapter 4
Yingxu Wang
An interactive motivation-attitude theory is developed based on the Layered Reference Model of the Brain (LRMB) and the Object-Attribute-Relation... Sample PDF
On the Cognitive Processes of Human Perception with Emotions, Motivations, and Attitudes
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Chapter 5
Qingyong Li, Zhiping Shi, Zhongzhi Shi
Sparse coding theory demonstrates that the neurons in the primary visual cortex form a sparse representation of natural scenes in the viewpoint of... Sample PDF
A Selective Sparse Coding Model with Embedded Attention Mechanism
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Chapter 6
Yingxu Wang
Theoretical research is predominately an inductive process, while applied research is mainly a deductive process. Both inference processes are based... Sample PDF
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Chapter 7
Douglas Griffith, Frank L. Greitzer
The purpose of this article is to re-address the vision of human-computer symbiosis as originally expressed by J.C.R. Licklider nearly a... Sample PDF
Neo-Symbiosis: The Next Stage in the Evolution of Human Information Interaction
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Chapter 8
Ray E. Jennings
Although linguistics may treat languages as a syntactic and/or semantic entity that regulates both language production and comprehension, this... Sample PDF
Language, Logic, and the Brain
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Chapter 9
Yingxu Wang, Guenther Ruhe
Decision making is one of the basic cognitive processes of human behaviors by which a preferred option or a course of actions is chosen from among a... Sample PDF
The Cognitive Process of Decision Making
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Chapter 10
Tiansi Dong
This chapter proposes a commonsense understanding of distance and orientation knowledge between extended objects, and presents a formal... Sample PDF
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Chapter 11
Natalia López, Manuel Núñez, Fernando L. Pelayo
In this chapter we present the formal language, stochastic process algebra (STOPA), to specify cognitive systems. In addition to the usual... Sample PDF
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Chapter 12
Yingxu Wang
Autonomic computing (AC) is an intelligent computing approach that autonomously carries out robotic and interactive applications based on goal- and... Sample PDF
Theoretical Foundations of Autonomic Computing
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Chapter 13
Witold Kinsner
Numerous attempts are being made to develop machines that could act not only autonomously, but also in an increasingly intelligent and cognitive... Sample PDF
Towards Cognitive Machines: Multiscale Measures and Analysis
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Chapter 14
Amar Ramdane-Cherif
Cognitive approach through the neural network (NN) paradigm is a critical discipline that will help bring about autonomic computing (AC). NN-related... Sample PDF
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Chapter 15
Lee Flax
We give an approach to cognitive modelling, which allows for richer expression than the one based simply on the firing of sets of neurons. The... Sample PDF
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Chapter 16
Yan Zhao, Yiyu Yao
Classification is one of the main tasks in machine learning, data mining, and pattern recognition. Compared with the extensively studied automation... Sample PDF
Interactive Classification Using a Granule Network
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Chapter 17
Mehdi Najjar, André Mayers
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A Cognitive Computational Knowledge Representation Theory
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Chapter 18
Du Zhang
A crucial component of an intelligent system is its knowledge base that contains knowledge about a problem domain. Knowledge base development... Sample PDF
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Chapter 19
Christine W. Chan
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Chapter 20
Václav Rajlich, Shaochun Xu
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Chapter 21
Witold Kinsner
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Chapter 22
Du Zhang, Witold Kinsner, Jeffrey Tsai, Yingxu Wang, Philip Sheu, Taehyung Wang
The 2005 IEEE International Conference on Cognitive Informatics (ICCI’05) was held during August 8th to 10th 2005 on the campus of University of... Sample PDF
Cognitive Informatics: Four Years in Practice
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Chapter 23
Yiyu Yao, Zhongzhi Shi, Yingxu Wang, Witold Kinsner, Yixin Zhong, Guoyin Wang
Cognitive informatics (CI) is a cutting-edge and multidisciplinary research area that tackles the fundamental problems shared by modern informatics... Sample PDF
Toward Cognitive Informatics and Cognitive Computers: A Report on IEEE ICCI'06
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About the Contributors