In this paper, we introduce a neural network-based decision table algorithm. We focus on the implementation details of the decision table algorithm when it is constructed using the neural network. Decision tables are simple supervised classifiers which, Kohavi demonstrated, can outperform state-of-the-art classifiers such as C4.5. We couple this power with the efficiency and flexibility of a bi...

In the paper, we present an empirical evaluation of five feature selection methods: ReliefF, random forest feature selector, sequential forward selection, sequential backward selection, and Gini index. Among the evaluated methods, the random forest feature selector has not yet been widely compared to the other methods. In our evaluation, we test how the implemented feature selection can affect ...

This study presents a novel watermarking algorithm for improving the security and robustness of hiding audio data in an image. Multi resolution discrete wavelet transform is used for embedding the audio watermark in an image. In this context, security is quantified from an information theoretic point of view by means of the equivocation and information leakage of the secret parameters. The sele...

A recent generalization of the Conley index to discrete multivalued dynamical systems without a continuous selector is motivated by applications data–driven dynamics. In present paper we continue program studying attractor–repeller pairs and Morse decompositions in this setting. particular, prove equation inequalities.

We consider the following sparse signal recovery (or feature selection) problem: given a design matrix X ∈ Rn×m (m À n) and a noisy observation vector y ∈ R satisfying y = Xβ∗ + 2 where 2 is the noise vector following a Gaussian distribution N(0, σI), how to recover the signal (or parameter vector) β∗ when the signal is sparse? The Dantzig selector has been proposed for sparse signal recovery w...

The selection of peptides for presentation at the surface of most nucleated cells by major histocompatibility complex class I molecules (MHC I) is crucial to the immune response in vertebrates. However, the mechanisms of the rapid selection of high affinity peptides by MHC I from amongst thousands of mostly low affinity peptides are not well understood. We developed computational systems models...

We describe a fast visual feature tracking system which takes advantage of dedicated hardware to perform the computationally intensive step of selection. A software system uses the output of the hardware selector to develop tracks using filtering, data association techniques, and image-based validation.

Small interfering RNA (siRNA) is used in functional genomics applications to decrease the expression of a target gene, which may yield a biological effect that suggests a function for the target gene. The siRNA design tool scans a target gene for candidate siRNA sequences that satisfy user-adjustable rules. Selected candidates are then screened to identify those siRNA sequences that are specifi...

High-dimensional data in many areas such as computer vision and machine learning brings in computational and analytical difficulty. Feature selection which select a subset of features from original ones has been proven to be effective and efficient to deal with high-dimensional data. In this paper, we propose a novel AutoEncoder Feature Selector (AEFS) for unsupervised feature selection. AEFS i...