Jul 1, 2011 · In this paper we show that lazy associative classifiers (LAC) are well calibrated using an MDL-based entropy minimization method.
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In this paper we show that lazy associative classifiers (LAC) are well calibrated using an MDL-based entropy minimization method.
[PDF] Calibrated lazy associative classification - Computer Science
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Classification is a popular machine learning task. Given an example x and a class c, a clas- sifier usually works by estimating the probability of x being ...
Abstract. Classification is an important problem in data mining. Given an ex- ample x and a class c, a classifier usually works by estimating the ...
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Dec 31, 2010 · Well calibrated classifiers are those able to provide accurate estimates of class membership probabilities, that is, the estimated probability ...
Well calibrated classi ers are those able to provide accurate estimates of class membership probabilities, that is, the estimated probability ˆ p(c|x) is close ...
In this paper we show that lazy associative classifiers (LAC) are accurate, and well calibrated using a well known, sound, entropy-minimization method. We ...
The document proposes using an entropy-minimization method to calibrate LACs and evaluates its effectiveness on real-world datasets, finding it outperforms ...
We introduce a batched lazy algorithm for supervised classification using decision trees. It avoids unnecessary visits to irrelevant nodes when it is used to ...
Apr 19, 2023 · Associative classification (AC) has been shown to outperform other methods of single-label classification for over 20 years.