Book ChapterDOI
Constructive Induction Using a Non-Greedy Strategy for Feature Selection
Arlindo L. Oliveira,Alberto Sangiovanni-Vincentelli +1 more
- pp 355-360
TLDR
A method for feature construction and selection that finds a minimal set of conjunctive features that are appropriate to perform the classification task and is able to achieve higher classification accuracy.Abstract:
We present a method for feature construction and selection that finds a minimal set of conjunctive features that are appropriate to perform the classification task For problems where this bias is appropriate, the method outperforms other constructive induction algorithms and is able to achieve higher classification accuracy The application of the method in the search for minimal multi-level boolean expressions is presented and analyzed with the help of some examplesread more
Citations
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Journal ArticleDOI
Feature Selection for Classification
Manoranjan Dash,Huan Liu +1 more
TL;DR: This survey identifies the future research areas in feature selection, introduces newcomers to this field, and paves the way for practitioners who search for suitable methods for solving domain-specific real-world applications.
Journal ArticleDOI
Toward integrating feature selection algorithms for classification and clustering
TL;DR: With the categorizing framework, the efforts toward-building an integrated system for intelligent feature selection are continued, and an illustrative example is presented to show how existing feature selection algorithms can be integrated into a meta algorithm that can take advantage of individual algorithms.
Journal ArticleDOI
Consistency-based search in feature selection
Manoranjan Dash,Huan Liu +1 more
TL;DR: An empirical study is conducted to examine the pros and cons of these search methods, give some guidelines on choosing a search method, and compare the classifier error rates before and after feature selection.
Proceedings ArticleDOI
Feature selection algorithms: a survey and experimental evaluation
TL;DR: This work assesses the performance of several fundamental algorithms found in the literature in a controlled scenario by taking into account the amount of relevance, irrelevance and redundance on sample data sets and a scoring measure ranks the algorithms.
Journal ArticleDOI
A Fast Clustering-Based Feature Subset Selection Algorithm for High-Dimensional Data
TL;DR: The results demonstrate that the FAST not only produces smaller subsets of features but also improves the performances of the four types of classifiers.
References
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Book
Computers and Intractability: A Guide to the Theory of NP-Completeness
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Book
Logic Minimization Algorithms for VLSI Synthesis
TL;DR: The ESPRESSO-IIAPL as discussed by the authors is an extension of the ESPRSO-IIC with the purpose of improving the efficiency of Tautology and reducing the number of blocks and covers.
Proceedings Article
The multi-purpose incremental learning system AQ15 and its testing application to three medical domains
TL;DR: The demonstration that by applying the proposed method of cover truncation and analogical matching, called TRUNC, one may drastically decrease the complexity of the knowledge base without affecting its performance accuracy is demonstrated.