Pub. online:1 Jan 2012Type:Research ArticleOpen Access
Volume 23, Issue 4 (2012), pp. 521–536
In a supervised learning, the relationship between the available data and the performance (what is learnt) is not well understood. How much data to use, or when to stop the learning process, are the key questions.
In the paper, we present an approach for an early assessment of the extracted knowledge (classification models) in the terms of performance (accuracy). The key questions are answered by detecting the point of convergence, i.e., where the classification model's performance does not improve any more even when adding more data items to the learning set. For the learning process termination criteria we developed a set of equations for detection of the convergence that follow the basic principles of the learning curve. The developed solution was evaluated on real datasets. The results of the experiment prove that the solution is well-designed: the learning process stopping criteria are not subjected to local variance and the convergence is detected where it actually has occurred.
Pub. online:1 Jan 2007Type:Research ArticleOpen Access
Volume 18, Issue 3 (2007), pp. 343–362
One of the tasks of data mining is classification, which provides a mapping from attributes (observations) to pre-specified classes. Classification models are built by using underlying data. In principle, the models built with more data yield better results. However, the relationship between the available data and the performance is not well understood, except that the accuracy of a classification model has diminishing improvements as a function of data size. In this paper, we present an approach for an early assessment of the extracted knowledge (classification models) in the terms of performance (accuracy), based on the amount of data used. The assessment is based on the observation of the performance on smaller sample sizes. The solution is formally defined and used in an experiment. In experiments we show the correctness and utility of the approach.
Pub. online:1 Jan 2003Type:Research ArticleOpen Access
Volume 14, Issue 3 (2003), pp. 277–288
In the paper, we present an algorithm that can be applied to protect data before a data mining process takes place. The data mining, a part of the knowledge discovery process, is mainly about building models from data. We address the following question: can we protect the data and still allow the data modelling process to take place? We consider the case where the distributions of original data values are preserved while the values themselves change, so that the resulting model is equivalent to the one built with original data. The presented formal approach is especially useful when the knowledge discovery process is outsourced. The application of the algorithm is demonstrated through an example.