discretization of the input data. The paper describes a Fast Class-Attribute Interdependence Maximization. (F-CAIM) algorithm that is an extension of the. MCAIM: Modified CAIM Discretization Algorithm for. Classification. Shivani V. Vora. (Research) Scholar. Department of Computer Engineering, SVNIT. CAIM (Class-Attribute Interdependence Maximization) is a discretization algorithm of data for which the classes are known. However, new arising challenges.
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Could you please send me the data directly? Balanced data sets information Data set Instances Attributes Real Integer Nominal Classes abalone 8 7 0 1 28 arrhythmia 0 73 16 glass 9 9 0 0 7 heart 13 1 4 8 2 ionosphere 33 32 0 1 2 iris 4 4 0 0 3 jm1 21 13 8 0 2 madelon 0 0 2 mc1 38 10 28 0 2 mfeat-factors 0 0 10 mfeat-fourier 76 76 0 0 10 mfeat-karhunen 64 64 0 0 10 mfeat-zernike 47 47 0 0 10 pc2 36 13 23 0 2 penbased 16 16 0 0 10 pendigits 16 0 16 0 10 pima 8 8 0 0 2 satimage 36 0 36 0 7 segment 19 19 0 0 7 sonar 60 60 0 0 2 spambase 57 57 0 0 2 spectrometer 0 2 48 texture 40 40 0 0 11 thyroid 21 6 0 15 3 vowel 13 11 0 2 11 waveform 40 40 0 0 3 winequality-red 11 11 0 0 11 winequality-white 11 11 0 0 CAIM class-attribute interdependence maximization is designed to discretize continuous data.
caij Aren’t the class label supposed to be a binary indicator matrix with 1ofK coding? The ur-CAIM was compared with 9 well-known discretization methods on 28 balanced, and 70 unbalanced data sets. I am not able to understand the class labels assigned to the Yeast dataset.
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However, new arising challenges such as the presence of unbalanced data sets, call for new algorithms capable of handling them, in addition to balanced data. I have a question regarding the algodithm labels. Hemanth Hemanth view profile. These data sets are very different in terms of their complexity, number of classes, number of attributes, number of instances, and unbalance ratio ratio of size of the majority class to minority class.
CAIM Discretization Algorithm – File Exchange – MATLAB Central
Learn About Live Editor. Select the China site in Chinese or English for best site performance. First, it generates more flexible discretization schemes while producing a small number of intervals.
Thanks for the code Guangdi Li. Updated 17 Oct Second, the quality of the intervals is improved based on the data classes distribution, which leads to better classification performance on balanced and, especially, unbalanced data. Updates 17 Oct 1. Other MathWorks country sites are not optimized for visits from your location. The results disccretization were contrasted through non-parametric statistical tests, which show that our proposal outperforms CAIM and many of the other methods on both types of data but disceetization on unbalanced data, which is its significant advantage.
The task of extracting knowledge from databases is quite often performed by machine learning algorithms.
ur-CAIM: Improved CAIM Discretization for Unbalanced and Balanced Data
You are now following this Submission You will see updates in your activity feed You may receive emails, depending on your notification preferences. Discover Live Editor Create scripts with code, output, and discretizatin text in a single executable document. Full results for each discretization and classification algorithm, and for each data set are available to download in CSV format.
I will answer you as soon as possible. These algorithms were used in Garcia et al.
ur-CAIM: An Improved CAIM Discretization Algorithm for Unbalanced and Balanced Data Sets
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Hi, I got a error, can u help me? Yu Li Yu Li view profile. The algorithm has been designed free-parameter and it self-adapts to the problem complexity and the data class distribution.
One can start with “ControlCenter.
If there is any problemplease let me know. Hello sir i am student of jntuk university.
Discretized data sets are available to download for each discretization method. Guangdi Li Guangdi Li view profile.