Typicality, Diversity, and Feature Pattern of an Ensemble

Authors:
A. K. C. Wong;T. S. Liu
Affiliations:
Biotechnology Program and Department of Electrical Engineering, Carnegie-Mellon University;-
Venue:
IEEE Transactions on Computers
Year:
1975

Citing 0
Cited 8

A Fuzzy Approach to Partitioning Continuous Attributes for Classification

IEEE Transactions on Knowledge and Data Engineering
A Discretization Process in Accordance with a Qualitative Ordered Output

Proceedings of the 2005 conference on Artificial Intelligence Research and Development
Pattern discovery for large mixed-mode database

CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Review:

The Knowledge Engineering Review
A decision-directed clustering algorithm for discrete data

IEEE Transactions on Computers
An effective discretization based on Class-Attribute Coherence Maximization

Pattern Recognition Letters
Gene selection by cooperative competition clustering

ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
Correlation maximisation-based discretisation for supervised classification

International Journal of Business Intelligence and Data Mining

Quantified Score

Hi-index	14.98

Visualization

Abstract

In this paper, issues concerning feature patterns in terms of both feature composition and feature interdependence are discussed, and the concepts of typicality and diversity of an ensemble are formulated. The features of the specimens investigated are organized in a two-dimensional array, called an observation matrix, with each row vector representing the ordered set of features of a specimen. An algorithm (based upon the proposed measures and statistical screening) is implemented for extracting feature patterns. In the algorithm, schemes for feature patterns and specimen reweighting are proposed to optimize the utilization of available information in the array, and to minimize possible bias caused by the uneven sampling of the ensemble. Two sets of real world data in the environmental and molecular biology areas are used to exemplify the physical meaning of the proposed measures as well as to demonstrate the operational feasibility and significance of this methodology in analyzing homologous ensemble which is subject to variable degrees of diversity.