Will the real iris data please stand up?

Bezdek, James C.; Keller, J.M.; Krishnapuram, Raghu; Kuncheva, Ludmila I.; Pal, Nikhil R.

doi:10.1109/91.771092

Cited by 108 publications

(51 citation statements)

References 4 publications

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“…We tested the algorithms of the previous sections on the classical Iris classiÿcation problem [2]. Each Iris exemplar has four features and is classiÿed into one of three categories.…”

Section: Simulation Resultsmentioning

confidence: 99%

Training radial basis neural networks with the extended Kalman filter

Simon

2002

Neurocomputing

126

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Radial basis function (RBF) neural networks provide attractive possibilities for solving signal processing and pattern classiÿcation problems. Several algorithms have been proposed for choosing the RBF prototypes and training the network. The selection of the RBF prototypes and the network weights can be viewed as a system identiÿcation problem. As such, this paper proposes the use of the extended Kalman ÿlter for the learning procedure. After the user chooses how many prototypes to include in the network, the Kalman ÿlter simultaneously solves for the prototype vectors and the weight matrix. A decoupled extended Kalman ÿlter is then proposed in order to decrease the computational e ort of the training algorithm. Simulation results are presented on reformulated radial basis neural networks as applied to the Iris classiÿcation problem. It is shown that the use of the Kalman ÿlter results in better learning than conventional RBF networks and faster learning than gradient descent.

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“…We tested the algorithms of the previous sections on the classical Iris classiÿcation problem [2]. Each Iris exemplar has four features and is classiÿed into one of three categories.…”

Section: Simulation Resultsmentioning

confidence: 99%

Training radial basis neural networks with the extended Kalman filter

Simon

2002

Neurocomputing

126

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“…The iris data is a common benchmark in classification and pattern recognition studies [2], [10], [25], [26]. It contains 50 measurements of four features from each of the three species Iris setosa, Iris versicolor, and Iris virginica [27].…”

Section: B Iris Data Classificationmentioning

confidence: 99%

GA-fuzzy modeling and classification: complexity and performance

Setnes

Roubos

2000

IEEE Trans. Fuzzy Syst.

436

219

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Abstract-The use of genetic algorithms (GAs) and other evolutionary optimization methods to design fuzzy rules for systems modeling and data classification have received much attention in recent literature. Authors have focused on various aspects of these randomized techniques, and a whole scale of algorithms have been proposed. We comment on some recent work and describe a new and efficient two-step approach that leads to good results for function approximation, dynamic systems modeling and data classification problems. First fuzzy clustering is applied to obtain a compact initial rule-based model. Then this model is optimized by a real-coded GA subjected to constraints that maintain the semantic properties of the rules. We consider four examples from the literature: a synthetic nonlinear dynamic systems model, the iris data classification problem, the wine data classification problem, and the dynamic modeling of a diesel engine turbocharger. The obtained results are compared to other recently proposed methods.

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“…[12]. This correspondence pointed to a series of minor errors in reported values of data collected by Anderson in 1935 [10] and reported first by Fisher in 1936 [11].…”

Section: Fisher's Iris Datamentioning

confidence: 94%

“…First, since custom codes for the standard and iterative G-K algorithms were written or modified in MATLAB (The MathWorks, Inc., Natick, MA) for this study, some measurement of their effectiveness was needed (the MATLAB function fcm was used as a baseline so validation of that code was deemed unnecessary), and the Iris data was used as a "debugging," validation and benchmarking tool. The second reason for validation was that in 1999 Bezdek published a correspondence indicating that there were multiple distinctly different versions of the Iris data that have been used as data sets in various published reports [12]. For this study, the original Iris data from [11] were carefully transcribed from the original work and independently checked for errors by a number of individuals to ensure that the correct data were, in fact, being used.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data

Miller

Nelson

Cannon

et al. 2009

Applied Computational Intelligence and Soft Computing

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Fuzzy clustering algorithms are helpful when there exists a dataset with subgroupings of points having indistinct boundaries and overlap between the clusters. Traditional methods have been extensively studied and used on real-world data, but require users to have some knowledge of the outcome a priori in order to determine how many clusters to look for. Additionally, iterative algorithms choose the optimal number of clusters based on one of several performance measures. In this study, the authors compare the performance of three algorithms (fuzzy c-means, Gustafson-Kessel, and an iterative version of Gustafson-Kessel) when clustering a traditional data set as well as real-world geophysics data that were collected from an archaeological site in Wyoming. Areas of interest in the were identified using a crisp cutoff value as well as a fuzzy α-cut to determine which provided better elimination of noise and non-relevant points. Results indicate that the α-cut method eliminates more noise than the crisp cutoff values and that the iterative version of the fuzzy clustering algorithm is able to select an optimum number of subclusters within a point set (in both the traditional and real-world data), leading to proper indication of regions of interest for further expert analysis

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Will the real iris data please stand up?

Abstract: This correspondence points out several published errors in replicates of the well-known Iris data, which was collected in 1935 by Anderson [1], but first published in 1936 by Fisher [2].

Cited by 108 publications

References 4 publications

Training radial basis neural networks with the extended Kalman filter

Training radial basis neural networks with the extended Kalman filter

GA-fuzzy modeling and classification: complexity and performance

Comparison of Fuzzy Clustering Methods and Their Applications to Geophysics Data

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