This paper focuses on enhancing the effectiveness of filter feature selection models from two aspects. First, feature-searching engine is modified based on optimization theory. Second, a point injection strategy is designed to improve the regularization capability of feature selection. The second topic is important, because overfitting is usually experienced. To evaluate the proposed strategies...

A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward oating selection (SFFS) algorithm, proposed by Pudil et al., dominates the other algorithms tested. We study the problem of choosing an optimal feature set for land use classi cation based on SAR satellite images using four di erent texture models. Pooling feat...

A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward floating selection (SFFS) algorithm, proposed by Pudil et al., dominates the other algorithms tested. We study the problem of choosing an optimal feature set for land use classification based on SAR satellite images using four different texture models. Pooling ...

Automatic Detection And Classification (ADAC) is a system to detect and classify the underwater objects for mine hunting applications. The segmentation, feature extraction, and classification are the main steps involved in the system. Two design issues in the system, the selection of the optimal classifier and the selection of the optimal feature subset. The comparison of classification systems...

Given the relatively small number of microarrays typically used in gene-expression-based classification, all of the data must be used to train a classifier and therefore the same training data is used for error estimation. The key issue regarding the quality of an error estimator in the context of small samples is its accuracy, and this is most directly analyzed via the deviation distribution o...

Most widely-used feature selection methods assume that features are either relevant in the whole instance space or irrelevant throughout. However, it can often be the case that features are relevant only in the context of other features (e.g., feature Y is relevant if feature X = 1, but irrelevant if X = 0). RC is a new feature selection algorithm that takes this into account, by potentially se...

We propose a novel feature selection algorithm for liver tissue pathological image classification. To improve the efficiency of feature selection, the same feature values of positive and negative samples are removed in rough selection. To obtain the optimal feature subset, a new heuristic search algorithm, which is called Maximum Minimum Backward Selection (MMBS), is proposed in precise selecti...

In this paper, a decision tree-based approach for recognizing stem and calyx regions of apples by computer vision is proposed. The method starts with background removal and object segmentation by thresholding. Statistical, textural and shape features are extracted from each segmented object and these features are introduced to two decision tree algorithms: CART and C4.5. Feature selection is ac...

A large number of algorithms have been proposed for doing feature subset selection. The goal of this paper is to evaluate the quality of feature subsets generated by the various algorithms, and also compare their computational requirements. Our results show that the sequential forward floating selection (SFFS) algorithm, proposed by Pudil et al., dominates the other algorithms tested. This pape...

In this paper we suggest feature selection and Principal Component Analysis as a way to analyze and compare corpora of emotional speech. To this end, a fast improvement of the Sequential Forward Floating Search algorithm is introduced, and subsequently extensive tests are run on a selection of French emotional language resources well suited for a first impression on general applicability. Tools...