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Study on the impact of partitioninduced dataset shift on kfold crossvalidation
 IEEE Transactions on Neural Networks and Learning Systems
, 2012
"... Abstract — Crossvalidation is a very commonly employed technique used to evaluate classifier performance. However, it can potentially introduce dataset shift, a harmful factor that is often not taken into account and can result in inaccurate performance estimation. This paper analyzes the prevalenc ..."
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Cited by 9 (4 self)
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Abstract — Crossvalidation is a very commonly employed technique used to evaluate classifier performance. However, it can potentially introduce dataset shift, a harmful factor that is often not taken into account and can result in inaccurate performance estimation. This paper analyzes the prevalence and impact of partitioninduced covariate shift on different kfold crossvalidation schemes. From the experimental results obtained, we conclude that the degree of partitioninduced covariate shift depends on the crossvalidation scheme considered. In this way, worse schemes may harm the correctness of a singleclassifier performance estimation and also increase the needed number of repetitions of crossvalidation to reach a stable performance estimation. Index Terms — Covariate shift, crossvalidation, dataset shift, partitioning.
Improving the H2MLVQ algorithm by the Cross Entropy Method
"... Abstract — This paper addresses the use of a stochastic optimization method called the Cross Entropy (CE) Method in the improvement of a recently proposed H2MLVQ (Harmonic to minimum LVQ) algorithm, this algorithm was proposed as an initialization insensitive variant of the well known Learning Vecto ..."
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Abstract — This paper addresses the use of a stochastic optimization method called the Cross Entropy (CE) Method in the improvement of a recently proposed H2MLVQ (Harmonic to minimum LVQ) algorithm, this algorithm was proposed as an initialization insensitive variant of the well known Learning Vector Quantization (LVQ) algorithm. This paper has two aims, the first aim is the use of the Cross Entropy (CE) Method to tackle the initialization sensitiveness problem associated with the original (LVQ) algorithm and its variants and the second aim is to use a weighted norm instead of the Euclidean norm in order to select the most relevant features. The results in this paper indicate that the CE method can successfully be applied to this kind of problems and efficiently generate high quality solutions. Also, good competitive numerical results on several datasets are reported. 1
of Bacillus
"... A genetic algorithmBayesian network approach for the analysis of metabolomics and spectroscopic data: application to the rapid identification of Bacillus spores and classification ..."
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A genetic algorithmBayesian network approach for the analysis of metabolomics and spectroscopic data: application to the rapid identification of Bacillus spores and classification
Accuracy Boosting Induction of Fuzzy Rules with Artificial Immune Systems
"... Abstract—The paper introduces accuracy boosting extension to a novel induction of fuzzy rules from raw data using Artificial Immune System methods. Accuracy boosting relies on fuzzy partition learning. The modified algorithm was experimentally proved to be more accurate for all learning sets contain ..."
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Abstract—The paper introduces accuracy boosting extension to a novel induction of fuzzy rules from raw data using Artificial Immune System methods. Accuracy boosting relies on fuzzy partition learning. The modified algorithm was experimentally proved to be more accurate for all learning sets containing noncrisp attributes. I.
is skewed
"... a r t i c l e i n f o ling with s class distributions, the classification problem becomes more difficult, specifically for correctly identifying the minori cepts within the data [11]. This issue is known as the class imbalance problem [21,38], in which there is an under sented class (positive) and a ..."
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a r t i c l e i n f o ling with s class distributions, the classification problem becomes more difficult, specifically for correctly identifying the minori cepts within the data [11]. This issue is known as the class imbalance problem [21,38], in which there is an under sented class (positive) and a majority class (negative). This problem is present in many realworld classification tas has been considered as a challenge within the Data Mining community [48].