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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Hemanth Hemanth view profile. First, it generates more flexible discretization schemes while producing a small number of intervals. One fold is used for pruning, the rest for growing the rules.

These algorithms were used in Garcia et al. Full results for each discretization and classification algorithm, and for each data set are available to download in CSV format. Updated 17 Oct Discover Live Editor Create scripts with code, output, and formatted text in a single executable document.

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Select a Web Site Choose a web site to get translated content where available and see local events and offers. If there is any problemplease let me know. Aren’t the class label supposed to be a binary indicator matrix with 1ofK coding? Select the China site in Chinese or English for best site performance.

Guangdi Li Guangdi Li view profile. The results obtained 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 especially on unbalanced data, which is its significant advantage.


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 discrefization 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 aogorithm 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 The majority of these algorithms can be applied only to data described by discrete numerical or nominal attributes features.

This code is based on paper: I have a question regarding the class labels.

ur-CAIM: An Improved CAIM Discretization Algorithm for Unbalanced and Balanced Data Sets

Learn About Live Editor. Discretized data sets are available to download for each discretization method. 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.

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The task of extracting knowledge from databases is quite often performed by machine learning algorithms. You are now following this Submission You will see updates alforithm your activity feed You may receive emails, depending on your notification preferences.

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Attempted to access B 0 ; index must be a positive integer or logical. Then I could test it and find the problem. I will answer caimm as soon as possible.

Could you please send me the data directly? Choose a web site to get translated content where available and see local events and offers. These data sets are very different in terms of their complexity, number of classes, number discretizatlon attributes, number of instances, and unbalance ratio ratio of size of the majority class to minority class.

I am not able to understand the class labels assigned to the Yeast dataset. Other MathWorks country sites are not optimized for visits from your location. Hello sir i am student of jntuk university.

ur-CAIM: Improved CAIM Discretization for Unbalanced and Balanced Data

Supervised discretization is one of basic data preprocessing techniques used in data mining. Yu Li Yu Li view profile. The ur-CAIM was compared with 9 well-known discretization methods on 28 balanced, and 70 unbalanced data sets. The algorithm has been designed free-parameter and it self-adapts to the problem complexity and the data class distribution.

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