Feature weighing methods are commonly used to find the relative significance among a set of features that are effectively used by the retrieval methods to search image sequences efficiently from large databases. As evidenced in the current literature, dynamic textures (image sequences with regular motion patterns) can be effectively modelled by a set of spatial and temporal motion distribution ...

In [1], Abegaz et. al compared hybrid genetic and evolutionary feature selection (GEFeS) and weighting (GEFeW) on feature sets obtained by Eigenface, LBP, and oLBP feature extraction methods. GEFeS and GEFeW were implemented using a Steady-State Genetic Algorithm (SSGA). In this paper, we extend the work performed in [1] and compared GEFeS and GEFeW implementations using SSGAs and Estimation of...

In automated text categorization, a system analyzes a natural-language document to decide whether it belongs in one or more of a group of pre-defined categories. The typical approach is to represent the documents using feature vectors, and inductively generate a classifier based on a training set of documents and their manually-assigned categories. Such a process ignores information on word ord...

Mass spectrometry (MS) is a key technique for the analysis and identification of proteins. A prediction of spectrum peak intensities from pre computed molecular features would pave the way to a better understanding of spectrometry data and improved spectrum evaluation. The goal is to model the relationship between peptides and peptide peak heights in MALDI-TOF mass spectra, only using the pepti...

Feature weighting algorithms try to solve a problem of great importance nowadays in machine learning: The search of a relevance measure for the features of a given domain. This relevance is primarily used for feature selection as feature weighting can be seen as a generalization of it, but it is also useful to better understand a problem’s domain or to guide an inductor in its learning process....

Prototype generation techniques have arisen as very competitive methods for enhancing the nearest neighbor classifier through data reduction. Within the prototype generation methodology, the methods of adjusting the prototypes’ positioning have shown an outstanding performance. Evolutionary algorithms have been used to optimize the positioning of the prototypes with promising results. prototype...

A dialogue system is a software program that enables a user to interact with a computer using a natural language (Kang et al. 2014). Since an essential task of the dialogue system is to understand what the user says, it must be able to determine the user’s intention indicated in the user’s utterance. A speech-act is a linguistic action and implies the user’s intention. Therefore, the dialogue s...

Article history: Received 9 August 2012 Received in revised form 13 June 2013 Accepted 23 September 2013 Available online 29 September 2013

For statistical modelling of multivariate binary data, such as text documents, datum instances are typically represented as vectors over a global vocabulary of attributes. Apart from the issue of high dimensionality, this also faces us with the problem of uneven importance of various attribute presences/absences. This problem has been largely overlooked in the literature, however it may create ...

With the fast increase of the documents, using Text Document Classification (TDC) methods has become a crucial matter. This paper presented a hybrid model of Invasive Weed Optimization (IWO) and Naive Bayes (NB) classifier (IWO-NB) for Feature Selection (FS) in order to reduce the big size of features space in TDC. TDC includes different actions such as text processing, feature extraction, form...