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Results 1 - 10
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7,743
Improved Use of Continuous Attributes in C4.5
- Journal of Artificial Intelligence Research
, 1996
"... A reported weakness of C4.5 in domains with continuous attributes is addressed by modifying the formation and evaluation of tests on continuous attributes. An MDL-inspired penalty is applied to such tests, eliminating some of them from consideration and altering the relative desirability of all test ..."
Abstract
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Cited by 281 (1 self)
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A reported weakness of C4.5 in domains with continuous attributes is addressed by modifying the formation and evaluation of tests on continuous attributes. An MDL-inspired penalty is applied to such tests, eliminating some of them from consideration and altering the relative desirability of all
Dynamic Discretization of Continuous Attributes
- In Proceedings of the Sixth Ibero-American Conference on AI
, 1998
"... Discretization of continuous attributes is an important task for certain types of machine learning algorithms. Bayesian approaches, for instance, require assumptions about data distributions. Decision Trees, on the other hand, require sorting operations to deal with continuous attributes, which ..."
Abstract
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Cited by 4 (0 self)
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Discretization of continuous attributes is an important task for certain types of machine learning algorithms. Bayesian approaches, for instance, require assumptions about data distributions. Decision Trees, on the other hand, require sorting operations to deal with continuous attributes, which
Compression-Based Discretization of Continuous Attributes
- Proceedings of the 12th International Conference on Machine Learning
, 1995
"... Discretization of continuous attributes into ordered discrete attributes can be beneficial even for propositional induction algorithms that are capable of handling continuous attributes directly. Benefits include possibly large improvements in induction time, smaller sizes of induced trees or rule s ..."
Abstract
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Cited by 48 (0 self)
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Discretization of continuous attributes into ordered discrete attributes can be beneficial even for propositional induction algorithms that are capable of handling continuous attributes directly. Benefits include possibly large improvements in induction time, smaller sizes of induced trees or rule
Estimating Attributes: Analysis and Extensions of RELIEF
, 1994
"... . In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies among them. Kira and Rendell (1992a,b) developed an algorithm called RELIEF, which was shown to be very efficient in estimating attributes. Origi ..."
Abstract
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Cited by 474 (25 self)
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. Original RELIEF can deal with discrete and continuous attributes and is limited to only two-class problems. In this paper RELIEF is analysed and extended to deal with noisy, incomplete, and multi-class data sets. The extensions are verified on various artificial and one well known real-world problem. 1
K.B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classication Learning. In:
- IJCAI.
, 1993
"... Abstract Since most real-world applications of classification learning involve continuous-valued attributes, properly addressing the discretization process is an important problem. This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuous-valued a ..."
Abstract
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Cited by 832 (7 self)
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Abstract Since most real-world applications of classification learning involve continuous-valued attributes, properly addressing the discretization process is an important problem. This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuous
Global discretization of continuous attributes as preprocessing for machine learning
- International Journal of Approximate Reasoning
, 1996
"... Abstract. Real-life data usually are presented in databases by real numbers. On the other hand, most inductive learning methods require small number of attribute values. Thus it is necessary to convert input data sets with continuous attributes into input data sets with discrete attributes. Methods ..."
Abstract
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Cited by 62 (4 self)
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Abstract. Real-life data usually are presented in databases by real numbers. On the other hand, most inductive learning methods require small number of attribute values. Thus it is necessary to convert input data sets with continuous attributes into input data sets with discrete attributes. Methods
Range Selectivity Estimation for Continuous Attributes
- In Proc. International Conference on Scientific and Statistical Database Management
, 1999
"... Many commercial database systems maintain histograms to efficiently estimate query selectivities as part of query optimization. Most work on histogram design is implicitly geared towards discrete or categorical attribute value domains. In this paper, we consider approaches that are better suited for ..."
Abstract
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Cited by 22 (0 self)
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for the continuous valued attributes commonly found in scientific and statistical databases. We propose two methods based on spline functions for estimating the selectivity of range queries over univariate and multivariate data. These methods are more accurate than histograms. As the results from our experiments
Handling Continuous Attributes in Ant Colony Classification Algorithms
- PROCEEDINGS OF THE 2009 IEEE SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE IN DATA MINING (CIDM 2009)
, 2009
"... Abstract — Most real-world classification problems involve continuous (real-valued) attributes, as well as, nominal (discrete) attributes. The majority of Ant Colony Optimisation (ACO) classification algorithms have the limitation of only being able to cope with nominal attributes directly. Extendin ..."
Abstract
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Cited by 5 (2 self)
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Abstract — Most real-world classification problems involve continuous (real-valued) attributes, as well as, nominal (discrete) attributes. The majority of Ant Colony Optimisation (ACO) classification algorithms have the limitation of only being able to cope with nominal attributes directly
Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences
- ACOUSTICS, SPEECH AND SIGNAL PROCESSING, IEEE TRANSACTIONS ON
, 1980
"... Several parametric representations of the acoustic signal were compared as to word recognition performance in a syllable-oriented continuous speech recognition system. The vocabulary in-cluded many phonetically similar monosyllabic words, therefore the emphasis was on ability to retain phonetically ..."
Abstract
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Cited by 1120 (2 self)
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Several parametric representations of the acoustic signal were compared as to word recognition performance in a syllable-oriented continuous speech recognition system. The vocabulary in-cluded many phonetically similar monosyllabic words, therefore the emphasis was on ability to retain
Discretizing Continuous Attributes While Learning Bayesian Networks
- In Proc. ICML
, 1996
"... We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in this method is a new metric based on the Minimal Description Length principle for choosing the threshold values for the dis ..."
Abstract
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Cited by 78 (4 self)
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We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in this method is a new metric based on the Minimal Description Length principle for choosing the threshold values
Results 1 - 10
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7,743
