Key Feature Selection Research Articles

Parallel noise eliminate: A parallel noise elimination algorithm for massive text categorization

Noise data in text are one of the main factors affecting the quality of text categorization. A parallel noise data elimination algorithm based on principal component analysis method and term frequency-inverse document frequency method for the noise data issue of massive text categorization is proposed. Five types of noise data which may occur during text categorization process are analyzed and summarized in this paper. Before text categorization, a redundant noise elimination algorithm based on key feature selection is presented for redundant noise features. During the process of text categorization, the error noise detection algorithm is given for inaccurate noise features. The proposed method is compared with other four typical noise processing methods in different noise ratios on two common corpora. The results show that the proposed method is feasible and can maintain more stable and excellent classification performance and lower running time.

A novel method for building regression tree models for QSAR based on artificial ant colony systems.

Among the multitude of learning algorithms that can be employed for deriving quantitative structure-activity relationships, regression trees have the advantage of being able to handle large data sets, dynamically perform the key feature selection, and yield readily interpretable models. A conventional method of building a regression tree model is recursive partitioning, a fast greedy algorithm that works well in many, but not all, cases. This work introduces a novel method of data partitioning based on artificial ants. This method is shown to perform better than recursive partitioning on three well-studied data sets.