Hybrid ACO and TOFA feature selection approach for text classification

With the highly increasing availability of text data on the Internet, the process of selecting an appropriate set of features for text classification becomes more important, for not only reducing the dimensionality of the feature space, but also for improving the classification performance. This paper proposes a novel feature selection approach to improve the performance of text classifier based on an integration of Ant Colony Optimization algorithm (ACO) and Trace Oriented Feature Analysis (TOFA). ACO is metaheuristic search algorithm derived by the study of foraging behavior of real ants, specifically the pheromone communication to find the shortest path to the food source. TOFA is a unified optimization framework developed to integrate and unify several state-of-the-art dimension reduction algorithms through optimization framework. It has been shown in previous research that ACO is one of the promising approaches for optimization and feature selection problems. TOFA is capable of dealing with large scale text data and can be applied to several text analysis applications such as text classification, clustering and retrieval. For classification performance yet effective, the proposed approach makes use of TOFA and classifier performance as heuristic information of ACO. The results on Reuters and Brown public datasets demonstrate the effectiveness of the proposed approach. © 2012 IEEE

Deriving query suggestions for site search

Modern search engines have been moving away from simplistic interfaces that aimed at satisfying a user's need with a single-shot query. Interactive features are now integral parts of web search engines. However, generating good query modification suggestions remains a challenging issue. Query log analysis is one of the major strands of work in this direction. Although much research has been performed on query logs collected on the web as a whole, query log analysis to enhance search on smaller and more focused collections has attracted less attention, despite its increasing practical importance. In this article, we report on a systematic study of different query modification methods applied to a substantial query log collected on a local website that already uses an interactive search engine. We conducted experiments in which we asked users to assess the relevance of potential query modification suggestions that have been constructed using a range of log analysis methods and different baseline approaches. The experimental results demonstrate the usefulness of log analysis to extract query modification suggestions. Furthermore, our experiments demonstrate that a more fine-grained approach than grouping search requests into sessions allows for extraction of better refinement terms from query log files. © 2013 ASIS&T

K-Space at TRECVid 2007

In this paper we describe K-Space participation in TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance. The first of the two systems was a ‘shot’ based interface, where the results from a query were presented as a ranked list of shots. The second interface was ‘broadcast’ based, where results were presented as a ranked list of broadcasts. Both systems made use of the outputs of our high-level feature submission as well as low-level visual features

The Best Trail Algorithm for Assisted Navigation of Web Sites

We present an algorithm called the Best Trail Algorithm, which helps solve the hypertext navigation problem by automating the construction of memex-like trails through the corpus. The algorithm performs a probabilistic best-first expansion of a set of navigation trees to find relevant and compact trails. We describe the implementation of the algorithm, scoring methods for trails, filtering algorithms and a new metric called \emph{potential gain} which measures the potential of a page for future navigation opportunities.Comment: 11 pages, 11 figure

Optimizing Associative Information Transfer within Content-addressable Memory

Original article can be found at: http://www.oldcitypublishing.com/IJUC/IJUC.htmlPeer reviewe

Moving towards adaptive search in digital libraries

Search applications have become very popular over the last two decades, one of the main drivers being the advent of the Web. Nevertheless, searching on the Web is very different to searching on smaller, often more structured collections such as digital libraries, local Web sites, and intranets. One way of helping the searcher locating the right information for a specific information need in such a collection is by providing well-structured domain knowledge to assist query modification and navigation. There are two main challenges which we will both address in this chapter: acquiring the domain knowledge and adapting it automatically to the specific interests of the user community. We will outline how in digital libraries a domain model can automatically be acquired using search engine query logs and how it can be continuously updated using methods resembling ant colony behaviour. © 2011 Springer-Verlag

Contour matching using ant colony optimization and curve evolution

Shape retrieval is a very important topic in computer vision. Image retrieval consists of selecting images that fulfil specific criteria from a collection of images. This thesis concentrates on contour-based image retrieval, in which we only explore the information located on the shape contour. There are many different kinds of shape retrieval methods. Most of the research in this field has till now concentrated on matching methods and how to achieve a meaningful correspondence. The matching process consist of finding correspondence between the points located on the designed contours. However, the huge number of incorporated points in the correspondence makes the matching process more complex. Furthermore, this scheme does not support computation of the correspondence intuitively without considering noise effect and distortions. Hence, heuristics methods are convoked to find acceptable solution. Moreover, some researches focus on improving polygonal modelling methods of a contour in such a way that the resulted contour is a good approximation of the original contour, which can be used to reduce the number of incorporated points in the matching. In this thesis, a novel approach for Ant Colony Optimization (ACO) contour matching that can be used to find an acceptable matching between contour shapes is developed. A polygonal evolution method proposed previously is selected to simplify the extracted contour. The main reason behind selecting this method is due to the use of a stopping criterion which must be predetermined. The match process is formulated as a Quadratic Assignment Problem (QAP) and resolved by using ACO. An approximated similarity is computed using original shape context descriptor and the Euclidean metric. The experimental results justify that the proposed approach is invariant to noise and distortions, and it is more robust to noise and distortion compared to the previously introduced Dominant Point (DP) Approach. This work serves as the fundamental study for assessing the Bender Test to diagnose dyslexic and non-dyslexic symptom in children