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25
Automatic Construction of Decision Trees from Data: A MultiDisciplinary Survey
 Data Mining and Knowledge Discovery
, 1997
"... Decision trees have proved to be valuable tools for the description, classification and generalization of data. Work on constructing decision trees from data exists in multiple disciplines such as statistics, pattern recognition, decision theory, signal processing, machine learning and artificial ne ..."
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Cited by 202 (1 self)
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Decision trees have proved to be valuable tools for the description, classification and generalization of data. Work on constructing decision trees from data exists in multiple disciplines such as statistics, pattern recognition, decision theory, signal processing, machine learning and artificial neural networks. Researchers in these disciplines, sometimes working on quite different problems, identified similar issues and heuristics for decision tree construction. This paper surveys existing work on decision tree construction, attempting to identify the important issues involved, directions the work has taken and the current state of the art. Keywords: classification, treestructured classifiers, data compaction 1. Introduction Advances in data collection methods, storage and processing technology are providing a unique challenge and opportunity for automated data exploration techniques. Enormous amounts of data are being collected daily from major scientific projects e.g., Human Genome...
An Active Testing Model for Tracking Roads in Satellite Images
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1995
"... We present a new approach for tracking roads from satellite images, and thereby illustrate a general computational strategy ("active testing") for tracking 1D structures and other recognition tasks in computer vision. Our approach is related to recent work in active vision on "where ..."
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Cited by 179 (6 self)
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We present a new approach for tracking roads from satellite images, and thereby illustrate a general computational strategy ("active testing") for tracking 1D structures and other recognition tasks in computer vision. Our approach is related to recent work in active vision on "where to look next" and motivated by the "divideandconquer" strategy of parlor games such as "Twenty Questions." We choose "tests" (matched filters for short road segments) one at a time in order to remove as much uncertainty as possible about the "true hypothesis" (road position) given the results of the previous tests. The tests are chosen online based on a statistical model for the joint distribution of tests and hypotheses. The problem of minimizing uncertainty (measured by entropy) is formulated in simple and explicit analytical terms. To execute this entropy testing rule we then alternate between data collection and optimization: at each iteration new image data are examined and a new entropy minimizat...
Joint Induction of Shape Features and Tree Classifiers
 IEEE Trans. PAMI
, 1997
"... We introduce a very large family of binary features for twodimensional shapes. The salient ones for separating particular shapes are determined by inductive learning during the construction of classi cation trees. There is a feature for every possible geometric arrangement of local topographic code ..."
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Cited by 83 (9 self)
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We introduce a very large family of binary features for twodimensional shapes. The salient ones for separating particular shapes are determined by inductive learning during the construction of classi cation trees. There is a feature for every possible geometric arrangement of local topographic codes. The arrangements express coarse constraints on relative angles and distances among the code locations and are nearly invariant to substantial a ne and nonlinear deformations. They are also partially ordered, which makes it possible to narrow the search for informative ones at each node of the tree. Di erent trees correspond to di erent aspects of shape. They are statistically weakly dependent due to randomization and are aggregated in a simple way. Adapting the algorithm to a shape family is then fully automatic once training samples are provided. As an illustration, we classify handwritten digits from the NIST database � the error rate is:7%.
Informationtheoretic measures for knowledge discovery and data mining
 In Karmeshu. Entropy Measures, Maximum Entropy Principle and Emerging Applications
, 2003
"... ..."
Learning pattern classification  A survey
 IEEE TRANS. INFORM. THEORY
, 1998
"... Classical and recent results in statistical pattern recognition and learning theory are reviewed in a twoclass pattern classification setting. This basic model best illustrates intuition and analysis techniques while still containing the essential features and serving as a prototype for many applic ..."
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Cited by 19 (4 self)
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Classical and recent results in statistical pattern recognition and learning theory are reviewed in a twoclass pattern classification setting. This basic model best illustrates intuition and analysis techniques while still containing the essential features and serving as a prototype for many applications. Topics discussed include nearest neighbor, kernel, and histogram methods, Vapnik–Chervonenkis theory, and neural networks. The presentation and the large (thogh nonexhaustive) list of references is geared to provide a useful overview of this field for both specialists and nonspecialists.
Letter Spirit: An Emergent Model of the Perception and Creation of Alphabetic Style
 Center for
, 1993
"... The Letter Spirit project is an attempt to model central aspects of human highlevel perception and creativity on a computer, focusing on the creative act of artistic letterdesign. The aim is to model the process of rendering the 26 lowercase letters of the roman alphabet in many different, interna ..."
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Cited by 9 (2 self)
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The Letter Spirit project is an attempt to model central aspects of human highlevel perception and creativity on a computer, focusing on the creative act of artistic letterdesign. The aim is to model the process of rendering the 26 lowercase letters of the roman alphabet in many different, internally coherent styles. Two important and orthogonal aspects of letterforms are basic to the project: the categorical sameness possessed by instances of a single letter in various styles (e.g., the letter `a' in Baskerville, Palatino, and Helvetica) and the stylistic sameness possessed by instances of various letters in a single style (e.g., the letters `a', `b', and `c' in Baskerville). Starting with one or more seed letters representing the beginnings of a style, the program will attempt to create the rest of the alphabet in such a way that all 26 letters share the same style, or spirit. Letters in the domain are formed exclusively from straight segments on a grid in order to make decisions ...
On informationtheoretic measures of attribute importance
 Proceedings of the Third PacificAsia Conference on Knowledge Discovery and Data Mining (PAKDD'99
, 1999
"... Abstract. An attribute is deemed important in data mining if it partitions the database such that previously unknown regularities are observable. Many informationtheoretic measures have been applied to quantify the importance of an attribute. In this paper, we summarize and critically analyze these ..."
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Cited by 9 (2 self)
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Abstract. An attribute is deemed important in data mining if it partitions the database such that previously unknown regularities are observable. Many informationtheoretic measures have been applied to quantify the importance of an attribute. In this paper, we summarize and critically analyze these measures. 1
Shape Recognition and Twenty Questions
 IN PROC. RECONNAISSANCE DES FORMES ET INTELLIGENCE ARTIFICIELLE (RFIA
, 1993
"... We formulate shape recognition as a coding problem. There is a finite list of possible "hypotheses"  shape classes and/or spatial positionings  and we wish to determine which one is true based on the results of various "tests," which are local image features. We use a decision ..."
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Cited by 9 (3 self)
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We formulate shape recognition as a coding problem. There is a finite list of possible "hypotheses"  shape classes and/or spatial positionings  and we wish to determine which one is true based on the results of various "tests," which are local image features. We use a decision tree: each interior node is assigned one of the tests and each terminal node is assigned one of the hypotheses. The assignment of tests, or "strategy," is recursive: along each branch choose the next test to remove as much uncertainty as possible (as measured by entropy) about the true hypothesis. In contrast to the standard approach of "hypothesize and test," there is no repeated elicitation of hypotheses; instead, the "indexing" is dynamic and stochastic. We gradually formulate specific conjectures as the evolving distribution on hypotheses becomes increasingly peaked. We apply this "twenty questions" approach to the recognition of two types of linear, deformable structures: handwritten numerals and roads i...
Randomized Inquiries About Shape; an Application to Handwritten Digit Recognition
, 1994
"... We describe an approach to shape recognition based on asking relational questions about the arrangement of landmarks, basically localized and oriented boundary segments. The questions are grouped into highly structured inquiries in the form of a tree. There are, in fact, many trees, each constructed ..."
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Cited by 4 (1 self)
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We describe an approach to shape recognition based on asking relational questions about the arrangement of landmarks, basically localized and oriented boundary segments. The questions are grouped into highly structured inquiries in the form of a tree. There are, in fact, many trees, each constructed from training data based on entropy reduction. The outcome of each tree is not a classification but rather a distribution over shape classes. The final classification is based on an aggregate distribution. The framework is nonEuclidean and there is no feature vector in the standard sense. Instead, the representation of the image data is graphical and each question is associated with a labeled subgraph. The ordering of the questions is highly constrained in order to maintain computational feasibility, and dependence among the trees is reduced by randomly subsampling from the available pool of questions. Experiments are reported on the recognition of handwritten digits. Although the amount ...