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Sampling and column (variable) selection are commonly used to gain insights that improve data mining performance on very large databases. This paper proposes an algorithm named CRPS (for Column Removal during Progressive Sampling) that integrates sampling and column reduction. This algorithm delivers a predictive model in less time than current approaches in tasks where not all columns are relevant. CRPS works in conjunction with predictive modeling algorithms that decide which columns to include in their models such as decision tree and logistic regression. The CRPS algorithm iteratively models on progressively larger samples and also removes some of the columns that were not used in the previous sample in order to reduce the amount of overall work. Other advantages of CRPS include the requirement of less memory to process the dataset, a ranking of relevance for each column, and the generation of simpler models based on fewer columns. The algorithm's time complexity is dependent on the number of columns in the final model and not the number of columns in the dataset. In the worst case when all columns are required by the model, CRPS does not increase the time complexity of progressive sampling, but could double the time of directly modeling all of the data. In more practical cases where some of columns are not relevant to the task, CRPS offers an advantage over traditional predictive modeling. Empirical results validate CRPS’s efficiency with no degradation in accuracy.
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