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. 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 algoruthm on 28 balanced, and 70 unbalanced data sets. Full results for each discretization and classification algorithm, and for each data set are available to download in CSV format.
Updates 17 Oct 1. These algorithms were used in Garcia et al. Updated 17 Oct I have a question regarding the class labels. Hi, I got a error, can u help me? 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.
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 acim of data but especially on unbalanced data, which is its significant advantage. Tags Add Tags classification data mining discretization.
ur-CAIM: An Improved CAIM Discretization Algorithm for Unbalanced and Balanced Data Sets
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The data riscretization are available to download balanced and unbalanced. Learn About Live Editor. 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 disvretization 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 discretizatipn 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 Thanks for the code Guangdi Li.
Comments and Ratings 4. Supervised discretization is one of basic data preprocessing techniques used in data mining. Attempted to access B 0 ; index must be a positive integer or logical. Select the China site in Chinese or English for best site performance. If there is any problemplease let me know. Discretized data sets cqim available to download for each discretization method. Other MathWorks country sites are not optimized for visits from your algoritbm.
Discover Live Editor Create scripts with code, output, and formatted text in a single executable document. Guangdi Li Guangdi Li view profile.
Select a Web Site Choose a web site to get translated content where available and see local events and offers. This code is based on paper: Hemanth Hemanth view profile. In the case of continuous attributes, there is a need for a discretization algorithm that transforms continuous attributes into discrete ones.
CAIM class-attribute interdependence maximization is designed to discregization continuous data.
Based on your location, we recommend that you select: The majority of these algorithms can be applied only to data described by discrete numerical or nominal attributes features. Could you please send me the data directly?
ur-CAIM: Improved CAIM Discretization for Unbalanced and Balanced Data
One fold is used for pruning, the rest for growing the rules. 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. Then I could test it and find the problem. Choose a web site to get translated content where available and see local events and offers. First, it generates more flexible discretization schemes while producing a small number of intervals.
One can start with “ControlCenter.
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The algorithm has been designed free-parameter and it self-adapts to the problem complexity and the data class distribution. I am not able to understand the class labels assigned to the Yeast dataset. Yu Li Yu Li view disxretization. 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.
Third, the runtime of the algorithm is lower than CAIM’s.