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...itioning of a continuous feature in O(m(log m + k 2 )) time, where k is the number of intervals and m is the number of instances. This method has yet to be tested experimentally. Vector Quantization (=-=Kohonen 1989-=-) is also related to the notion of discretization. This method attempts to partition an N-dimensional continuous space into a Voronoi Tessellation and then represent the set of points in each region b...

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...ne continuous feature. For the datasets that had more than 3000 test instances, we ran a single train/test experiment and report the theoretical standard deviation estimated using the Binomial model (=-=Kohavi 1995-=-). For the remaining datasets, we ran five-fold cross-validation and report the standard deviation of the cross-validation. Table 2 describes the datasets with the last column showing the accuracy of ...

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... transformations for a learning algorithm, and no careful study of how this discretization affects the learning process is performed (Weiss & Kulikowski 1991). In decision tree methods, such as C4.5 (=-=Quinlan 1993-=-), continuous values are discretized during the learning process. The advantages of discretizing during the learning process have not yet been shown. In this paper, we include such a comparison. Other...

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...istinct observed values for each attribute. The heuristic was chosen based on examining S-plus's histogram binning algorithm (Spector 1994). We chose sixteen datasets from the U.C. Irvine repository (=-=Murphy & Aha 1994-=-) that each had at least one continuous feature. For the datasets that had more than 3000 test instances, we ran a single train/test experiment and report the theoretical standard deviation estimated ...

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...ization methods require some parameter, k, indicating the maximum number of intervals to produce in discretizing a feature. Static methods, such as binning, entropy-based partitioning (Catlett 1991b, =-=Fayyad & Irani 1993-=-, Pfahringer 1995), and the 1R algorithm (Holte 1993), perform one discretization pass of the data for each feature and determine the value of k for each feature independent of the other features. Dyn...

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...imum number of intervals to produce in discretizing a feature. Static methods, such as binning, entropy-based partitioning (Catlett 1991b, Fayyad & Irani 1993, Pfahringer 1995), and the 1R algorithm (=-=Holte 1993-=-), perform one discretization pass of the data for each feature and determine the value of k for each feature independent of the other features. Dynamic methods conduct a search through the space of p...

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...non considering the fact that C4.5 is capable of locally discretizing features. 1 Introduction Many algorithms developed in the machine learning community focus on learning in nominal feature spaces (=-=Michalski & Stepp 1983-=-, Kohavi 1994). However, many real-world classi cation tasks exist that involve continuous features where such algorithms could not be applied unless the continuous features are rst discretized. Conti...

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...tion method and did not signi cantly degrade on any dataset, although it did decrease slightly on some. The entropy-based discretization is a global method and does not su er from data fragmentation (=-=Pagallo & Haussler 1990-=-). Since there is no signi cantDataset C4.5 Continuous Bin-log ` Entropy 1RD Ten Bins 1 anneal 91.65 1.60 90.32 1.06 89.65 1.00 87.20 1.66 89.87 1.30 2 australian 85.36 0.74 84.06 0.97 85.65 1.82 85....

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...tting partition boundaries, it is likely that classification information will be lost by binning as a result of combining values that are strongly associated with different classes into the same bin (=-=Kerber 1992-=-). In some cases this could make effective classification much more difficult. A variation of equal frequency intervals---maximal marginal entropy--- adjusts the boundaries to decrease entropy at each...

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... In this paper, we include such a comparison. Other reasons for variable discretization, aside from the algorithmic requirements mentioned above, include increasing the speed of induction algorithms (=-=Catlett 1991-=-b) and viewing General Logic Diagrams (Michalski 1978) of the induced classifier. In this paper, we address the effects of discretization on learning accuracy by comparing a range of discretization me...

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... In this paper, we include such a comparison. Other reasons for variable discretization, aside from the algorithmic requirements mentioned above, include increasing the speed of induction algorithms (=-=Catlett 1991-=-b) and viewing General Logic Diagrams (Michalski 1978) of the induced classifier. In this paper, we address the effects of discretization on learning accuracy by comparing a range of discretization me...

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...soever and is thus an unsupervised discretization method. 3.2 Holte's 1R Discretizer Holte (1993) describes a simple classi er that induces one-level decision trees, sometimes called decision stumps (=-=Iba & Langley 1992-=-). In order to properly deal with domains that contain continuous valued features, a simple supervised discretization method is given. This method, referred to here as 1RD (OneRule Discretizer), sorts...

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...al vs. local, supervised vs. unsupervised, andstatic vs. dynamic. Local methods, as exempli ed by C4.5, produce partitions that are applied to localized regions of the instance space. Global methods (=-=Chmielewski & Grzymala-Busse 1994-=-), such as binning, produce a mesh over the entire n-dimensional continuous instance space, where each feature is partitioned into regions independent of the other attributes. The mesh contains Qn i=1...

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... that C4.5 is capable of locally discretizing features. 1 Introduction Many algorithms developed in the machine learning community focus on learning in nominal feature spaces (Michalski & Stepp 1983, =-=Kohavi 1994-=-). However, many real-world classification tasks exist that involve continuous features where such algorithms could not be applied unless the continuous features are first discretized. Continuous vari...

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...re some parameter, k, indicating the maximum number of intervals to produce in discretizing a feature. Static methods, such as binning, entropy-based partitioning (Catlett 1991b, Fayyad & Irani 1993, =-=Pfahringer 1995-=-), and the 1R algorithm (Holte 1993), perform one discretization pass of the data for each feature and determine the value of k for each feature independent of the other features. Dynamic methods cond...

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... reasons for variable discretization, aside from the algorithmic requirements mentioned above, include increasing the speed of induction algorithms (Catlett 1991b) and viewing General Logic Diagrams (=-=Michalski 1978-=-) of the induced classifier. In this paper, we address the effects of discretization on learning accuracy by comparing a range of discretization methods using C4.5 and a Naive Bayes classifier. The Na...

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...imum number of intervals to produce in discretizing a feature. Static methods, such as binning, entropy-based partitioning (Catlett 1991b, Fayyad & Irani 1993, Pfahringer 1995), and the 1R algorithm (=-=Holte 1993-=-), perform one discretization pass of the data for each feature and determine the value of k for each feature independent of the other features. Dynamic methods conduct a search through the space of p...