Building Hierarchical Classifiers Using Class Proximity
Published 7 September 1999
Ke Wang, Senqiang Zhou, Shiang Chen Liew
Citations118
Generate an AI Snapshot to get a quick, structured summary of this paper.
Study Snapshot
ObjectiveStudy objective
MethodsResearch methodology
PopulationPopulation studied
Sample sizeSample sizes
OutcomesStudy outcomes here
ResultsStudy results comes here
LimitationsResearch study limitations comes here
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
The notion of class proximity is proposed and cast the hierarchical classi cation as a at classiCation with the class proximity modeling the closeness of classes.
Abstract
In this paper, we address the need to automatically classify text documents into topic hierarchies like those in ACM Digital Library and Yahoo!. The existing local approach constructs a classifier at each split of the topic hierarchy. However, the local approach does not address the closeness of classification in hierarchical classification where the concern often is how close a classification is, rather than simply correct or wrong. Also, the local approach puts its bet on classification at higher levels where the classification structure often diminishes. To address these issues, we propose the notion of class proximity and cast the hierarchical classification as a flat classification with the class proximity modeling the closeness of classes. Our approach is global in that it constructs a single classifier based on the global information about all classes and class proximity. We leverage generalized association rules as the rule/feature space to address several ...
Keywords
Computer Science
C4.5: Programs for Machine Learning
23,665 Citations1992J. R. Quinlan
A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Mining association rules between sets of items in large databases
14,720 Citations1993Rakesh Agrawal, Tomasz Imieliński +1 more
An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
Fast algorithms for mining association rules
10,739 Citations1998Rakesh Agrawal, Ramakrishnan Srikant
A Comparative Study on Feature Selection in Text Categorization
4,766 Citations1997Yiming Yang, Jan Pedersen
DF thresholding, the simplest method with the lowest cost in computation, can be reliably used instead of IG or CHI when the computation of these measures are too expensive, and strong correlations between the DF, IG and CHI values of a term are found.
Integrating classification and association rule mining
2,223 Citations1998Bing Liu, Wynne Hsu +1 more
The integration is done by focusing on mining a special subset of association rules, called class association rules (CARs), and shows that the classifier built this way is more accurate than that produced by the state-of-the-art classification system C4.5.
Future Generation Computer SystemsMining generalized association rules
1,617 Citations1997Ramakrishnan Srikant, Rakesh Agrawal
A new interest-measure for rules which uses the information in the taxonomy is presented, and given a user-specified “minimum-interest-level”, this measure prunes a large number of redundant rules.
Discovery of Multiple-Level Association Rules from Large Databases
920 Citations1995Jiawei Han, Yongjian Fu
A top-down progressive deepening method is developed for mining multiplelevel association rules from large transaction databases by extension of some existing association rule mining techniques.
Hierarchically Classifying Documents Using Very Few Words
840 Citations1997Daphne Koller, Mehran Sahami
This work proposes an approach that utilizes the hierarchical topic structure to decompose the classification task into a set of simpler problems, one at each node in the classification tree, which can be solved accurately by focusing only on a very small set of features, those relevant to the task at hand.
Enhanced hypertext categorization using hyperlinks
775 Citations1998Soumen Chakrabarti, Byron Dom +1 more
This work has developed a text classifier that misclassified only 13% of the documents in the well-known Reuters benchmark; this was comparable to the best results ever obtained and its technique also adapts gracefully to the fraction of neighboring documents having known topics.
A comparison of classifiers and document representations for the routing problem
451 Citations1995Hinrich Schütze, David A. Hull +1 more
This paper considers three classification techniques which have decision rules that are derived via explicit error minimization linear discriminant analysis, logistic regression, and neuraf networks, and finds that features based on latent semantic indexing are more effective for techniques such aslinear discriminant anaf-ysis and logistic regressors, which have no way to protect against overfitting.
IEEE Transactions on Knowledge and Data EngineeringAn information theoretic approach to rule induction from databases
357 Citations1992Padhraic Smyth, R.M. Goodman
An algorithm for the induction of rules from examples is introduced, which is novel in the sense that it not only learns rules for a given concept, but it simultaneously learns rules relating multiple concepts.
The VLDB JournalScalable feature selection, classification and signature generation for organizing large text databases into hierarchical topic taxonomies
230 Citations1998Soumen Chakrabarti, Byron Dom +2 more
An automatic system that starts with a small sample of the corpus in which topics have been assigned by hand, and then updates the database with new documents as the corpus grows, assigning topics to these new documents with high speed and accuracy is described.
Using Taxonomy, Discriminants, and Signatures for Navigating in Text Databases
130 Citations1997Soumen Chakrabarti, Byron Dom +2 more
This work uses techniques from statistical pattern recognition to efficiently separate the feature words or discriminants from the noise words at each node of the taxonomy, and builds a multi-level classifier that has a small model size and is very fast.
National Conference on Artificial IntelligenceAn efficient algorithm for finding optimal gain-ratio multiple-split tests on hierarchical attributes in decision tree learning
10 Citations1996Hussein Almuallim, Yasuhiro Akiba +1 more
An efficient algorithm is introduced for solving the problem of finding a multiple-split test defined on x that maximizes Quinlan's gain-ratio measure for a set of training examples S and a tree-structured attribute x.