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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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

Choose a web site to get translated content where available and see local events and offers. Hemanth Hemanth view profile. Discretized data sets are available to download for each discretization method. You are now following this Submission You will see updates in cwim activity feed You may receive emails, depending on your notification preferences.

I have a question regarding the class labels. This code is based on paper: Updated 17 Oct In the case of continuous attributes, there is a need for a discretization algorithm that transforms continuous attributes into discrete ones.

Hello sir i am student of jntuk university. 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.


Aren’t the class label supposed to be a binary indicator matrix with 1ofK coding? Discover Live Editor Create scripts with altorithm, output, and formatted text in a single executable document.

Updates 17 Oct 1. Could you please send me the data directly? Then I could test it and find the problem. Other MathWorks country sites are not optimized for visits from your location.

Guangdi Li Guangdi Li view profile. These algorithms were used in Garcia et al.

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

Algorith, Li Yu Li 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. Learn About Live Editor. Balanced data sets information Data set Instances Attributes Real Discretiaztion 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 discreization 0 0 2 mc1 38 10 discrettization 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 However, new arising challenges such as the presence of unbalanced data sets, call for new algorithms capable discretizaiton handling them, in addition to balanced data.


Tags Add Tags classification data mining discretization. The data sets are available to download balanced and unbalanced. Full results for each discretization and classification algorithm, and for each data set are available to download in CSV format.

CAIM class-attribute interdependence maximization is designed to discretize continuous data. Select the China site in Chinese or English for best site performance. Thanks for the code Guangdi Li. Select a Web Site Choose a web site to get translated content where available and see local events algorithj offers.

The ur-CAIM was compared with 9 well-known discretization methods on 28 algorithmm, and 70 unbalanced data sets. First, it generates more flexible discretization schemes while producing a small number of intervals.

If there is any problemplease let me know. Hi, I got a error, can u help me? One can start with “ControlCenter. Attempted to access B 0 ; index must be a positive integer or logical.

I will answer you as soon as possible.

I am not able to understand the class labels assigned to the Yeast dataset.