Edgar Acuña

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The presence of missing values in a dataset can affect the performance of a classifier constructed using that dataset as a training sample. Several methods have been proposed to treat missing data and the one used more frequently is deleting instances containing at least one missing value of a feature. In this paper we carry out experiments with twelve(More)
An outlier is an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism (Hawkins, 1980). Outlier detection has many applications, such as data cleaning, Fraud detection and network intrusion. The existence of outliers can indicate individuals or groups that have behavior very different(More)
An outlier is an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism. Outlier detection has many applications, such as data cleaning, fraud detection and network intrusion. The existence of outliers can indicate individuals or groups that exhibit a behavior that is very different(More)
Data preprocessing is a step of the Knowledge discovery in databases (KDD) process that reduces the complexity of the data and offers better conditions to subsequent analysis. Rough sets theory, where sets are approximated using elementary sets, is a different approach for developing methods for the data preprocessing process. In this paper Rough sets(More)
Nonparametric supervised classifiers are interesting because they do not require distributional assumptions for the class conditional density, such as normality or equal covariance. However their use is not widespread because it takes a lot of time to compute them due to the intensive use of the available data. On the other hand bundling classifiers to(More)
Many applications of automatic document classification require learning accurately with little training data. The semi-supervised classification technique uses labeled and unlabeled data for training. This technique has shown to be effective in some cases; however, the use of unlabeled data is not always beneficial. On the other hand, the emergence of web(More)
An outlier is an observation that deviates so much from other observations as to arouse suspicion that it was generated by a different mechanism (Hawkins, 1980). Outlier detection has many applications, such as data cleaning, fraud detection and network intrusion. The existence of outliers can indicate individuals or groups that have behavior very different(More)