in this paper, we propose a new gene selection algorithm based on shuffled frog leaping algorithm that is called sfla-fs. the proposed algorithm is used for improving cancer classification accuracy. most of the biological datasets such as cancer datasets have a large number of genes and few samples. however, most of these genes are not usable in some tasks for example in cancer classification. ...

Feature selection has improved the performance of text clustering. Global feature selection tries to identify a single subset of features which are relevant to all clusters. However, the clustering process might be improved by considering different subsets of features for locally describing each cluster. In this work, we introduce the method ZOOM-IN to perform local feature selection for partit...

We investigate single-view algorithms as an alternative to multi-view algorithms for weakly supervised learning for natural language processing tasks without a natural feature split. In particular, we apply co-training, self-training, and EM to one such task and find that both selftraining and FS-EM, a new variation of EM that incorporates feature selection, outperform cotraining and are compar...

Feature Selection (FS) is viewed as an important preprocessing step for pattern recognition, machine learning, and data mining. Most existing FS methods based on rough set theory use the dependency function for evaluating the goodness of a feature subset. However, these FS methods may unsuccessfully be applied on dataset with noise, which determine only information from a positive region but ne...

Simultaneous selection of the number of clusters and of a relevant subset of features is part of data mining challenges. A new approach is proposed to address this difficult issue. It takes benefits of both two-levels clustering approaches and wrapper features selection algorithms. On the one hands, the former enhances the robustness to outliers and to reduce the running time of the algorithm. ...

In text categorization, feature selection (FS) is a strategy that aims at making text classifiers more efficient and accurate. However, when dealing with a new task, it is still difficult to quickly select a suitable one from various FS methods provided by many previous studies. In this paper, we propose a theoretic framework of FS methods based on two basic measurements: frequency measurement ...

The research related to age estimation using face images has become increasingly important. We propose an age estimator using two kinds of local features, the gradient features which well describe the local characteristic, and the Gabor wavelets which reflect the multi-scale directional information. The RealAdaBoost algorithm with a complexity penalty term in the feature selection module is app...

We are moving into the age of 'Big Data' in biomedical research and bioinformatics. This trend could be encapsulated in this simple formula: D = S * F, where the volume of data generated (D) increases in both dimensions: the number of samples (S) and the number of sample features (F). Frequently, a typical omics classification includes redundant and irrelevant features (e.g. genes or proteins) ...

The ever increasing need for accurate burned area mapping has led to a number of studies that focus on improving the mapping accuracy and effectiveness. In this work, we investigate the influence of derivative spectral and spatial features on accurately mapping recently burned areas using VHR IKONOS imagery. Our analysis considers both pixel and object-based approaches, using two advanced image...