Feature selection can significantly be decisive when analyzing high dimensional data, especially with a small number of samples. Feature extraction methods do not have decent performance in these conditions. With small sample sets and high dimensional data, exploring a large search space and learning from insufficient samples becomes extremely hard. As a result, neural networks and clustering a...

different approaches have been proposed for feature selection to obtain suitable features subset among all features. these methods search feature space for feature subsets which satisfies some criteria or optimizes several objective functions. the objective functions are divided into two main groups: filter and wrapper methods.  in filter methods, features subsets are selected due to some measu...

For a broad-topic and ambiguous query, different users may have different search goals when they submit it to a search engine. The inference and analysis of user search goals can be very useful in improving search engine relevance and user experience. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. While the efficiency concerns the time ...

In machine learning, feature selection is preprocessing step and can be effectively reduce high dimensional data, remove irrelevant data, increase learning accuracy, and improve result comprehensibility. High dimensionality of data take over efficiency and effectiveness points of view in feature selection algorithm. Efficiency stands required time to find a subset of features, and the effective...

Several technical challenges are related to data collection, inverse modeling, model fusion, and integrated interpretations in the exploration of geophysics. A fundamental problem geophysical interpretation is proper geological understanding multiple inverted physical property images. Tackling this requires high-dimensional techniques for extracting information from modeled In study, we develop...

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...