This paper argues against the use of phonological underspecification in feature matrices on the basis of speech error data. Stemberger 1991 argues that phonological underspecification influences the similarity of phonemes. He claims underspecified features do not count toward similarity, based on an analysis of phoneme confusions in a naturally occurring speech error corpus. Using the same corp...

Feature interaction is an important issue in feature subset selection. However, most of the existing algorithms only focus on dealing with irrelevant and redundant features. In this paper, a propositional FOIL rule based algorithm FRFS, which not only retains relevant features and excludes irrelevant and redundant ones but also considers feature interaction, is proposed for selecting feature su...

Neural networks ensemble (NNE) is becoming an ad hoc topic in the machine learning community. However, redundant features will hurt the performance of NNE, so feature selection methods are developed to remove a part of the redundant features. Instead of only removing the features, multi-task learning can employ the removed redundant information to improve the prediction accuracy. The previous s...

—The paper presents features and implementation of a shared redundant approach to increase the reliability of networked control systems. Common approaches based on redundant components in control system use passive or active redundancy. We deal with quasi-redundant subsystems (shared redundancy) whereas basic features are introduced in the paper. This type of redundancy offers several important...

Redundant and irrelevant features in high dimensional data increase the complexity in underlying mathematical models. It is necessary to conduct pre-processing steps that search for the most relevant features in order to reduce the dimensionality of the data. This study made use of a meta-heuristic search approach which uses lightweight random simulations to balance between the exploitation of ...

In previous work we proposed an algorithm, REFER, for removing logically redundant features in a dataset consisting of Boolean examples, each labelled with one of any number of possible class labels. We define redundant features as those which can be removed without compromising the learning of a classification rule. Such redundant features are said to be covered by another present feature. Dis...

Feature selection involves the process of selecting a subset of relevant features that produces the result as the original set of features. The central assumption of using a feature selection technique in high dimensional data is that the data may contain many redundant or irrelevant features. Microarray dataset may also contain a huge number of redundant (insignificant) and irrelevant features...

Intrusion detection is one of the main challenges in wireless systems especially in Internet of things (IOT) based networks. There are various attack types such as probe, denial of service, remote to local and user to root. In addition to known attacks and malicious behaviors, there are various unknown attacks that some of them have similar behavior with respect to each other or mimic the norma...

The purpose of feature selection is to identify the relevant and non-redundant features from a dataset. In this article, the feature selection problem is organized as a graph-theoretic problem where a feature-dissimilarity graph is shaped from the data matrix. The nodes represent features and the edges represent their dissimilarity. Both nodes and edges are given weight according to the feature...