Simultaneous feature selection and feature weighting using Hybrid Tabu Search/K-nearest neighbor classifier

Feature selection and feature weighting are useful techniques for improving the classification accuracy of K-nearest-neighbor (K-NN) rule. The term feature selection refers to algorithms that select the best subset of the input feature set. In feature weighting, each feature is multiplied by a weight value proportional to the ability of the feature to distinguish pattern classes. In this paper, a novel hybrid approach is proposed for simultaneous feature selection and feature weighting of K-NN rule based on Tabu Search (TS) heuristic. The proposed TS heuristic in combination with K-NN classifier is compared with several classifiers on various available data sets. The results have indicated a significant improvement in the performance in classification accuracy. The proposed TS heuristic is also compared with various feature selection algorithms. Experiments performed revealed that the proposed hybrid TS heuristic is superior to both simple TS and sequential search algorithms. We also present results for the classification of prostate cancer using multispectral images, an important problem in biomedicine.

Job Search Methods and Outcomes

Using Portuguese data, this paper investigates the effects of job search methods on escape rates from unemployment and of job-finding methods on earnings. The effectiveness of the job search process is also evaluated in terms of the periodicity of the resulting job match. Emphasis is accorded the role of the public employment service. Despite its frequency as a search vehicle, the state employment agency is shown to have a low hit rate, and to lead to lower-paying, shorter-lasting jobs.

Paralytic shellfish poisoning detection by surface plasmon resonance-based biosensors in shellfish matrixes.

The detection of paralytic shellfish poisoning (PSP) toxins in contaminated shellfish is essential for human health preservation. Ethical and technical reasons have prompted the search for new detection procedures as an alternative to the mouse bioassay. On the basis of the detection of molecular interactions by surface plasmon resonance (SPR) biosensors, an inhibition assay was developed using an anti-GTX2/3 antibody (GT13-A) and a saxitoxin-CM5 chip. This assay allowed for quantification of saxitoxin (STX), decarbamoyl saxitoxin (dcSTX), gonyautoxin 2,3 (GTX2/3), decarbamoyl gonyautoxin 2,3 (dcGTX2/3), gonyautoxin 5 (GTX5), and C 1,2 (C1/2) at concentrations from 2 to 50 ng/mL. The interference of five shellfish matrixes with the inhibition assay was analyzed. Mussels, clams, cockles, scallops, and oysters were extracted with five published methods. Ethanol extracts and acetic acid/heat extracts (AOAC Lawrence method) performed adequately in terms of surface regeneration and baseline interference, did not inhibit antibody binding to the chip surface significantly, and presented STX calibration curves similar to buffer controls in all matrixes tested. Hydrochloric acid/heat extracts (AOAC mouse bioassay method) presented surface regeneration problems, and although ethanol-acetic acid/dichloromethane extracts performed well, they were considered too laborious for routine sample testing. Overall the best results were obtained with the ethanol extraction method with calibration curves prepared in blank matrix extracts. STX recovery rate with the ethanol extraction method was 60.52 ± 3.72%, with variations among species. The performance of this biosensor assay in natural samples, compared to two AOAC methods for PSP toxin quantification (mouse bioassay and HPLC), suggests that this technology can be useful as a PSP screening assay. In summary, the GT13-A-STX chip inhibition assay is capable of PSP toxin detection in ethanol shellfish extracts, with sufficient sensitivity to quantify the toxin in the range of the European regulatory limit of 80 g/100 g of shellfish meat.

A Survey of Search Methodologies and Automated System Development for Examination Timetabling

Exam timetabling is one of the most important administrative activities that takes place in academic institutions. In this paper we present a critical discussion of the research on exam timetabling in the last decade or so. This last ten years has seen an increased level of attention on this important topic. There has been a range of significant contributions to the scientific literature both in terms of theoretical andpractical aspects. The main aim of this survey is to highlight the new trends and key research achievements that have been carried out in the last decade.We also aim to outline a range of relevant important research issues and challenges that have been generated by this body of work.

We first define the problem and review previous survey papers. Algorithmic approaches are then classified and discussed. These include early techniques (e.g. graph heuristics) and state-of-the-art approaches including meta-heuristics, constraint based methods, multi-criteria techniques, hybridisations, and recent new trends concerning neighbourhood structures, which are motivated by raising the generality of the approaches. Summarising tables are presented to provide an overall view of these techniques. We discuss some issues on decomposition techniques, system tools and languages, models and complexity. We also present and discuss some important issues which have come to light concerning the public benchmark exam timetabling data. Different versions of problem datasetswith the same name have been circulating in the scientific community in the last ten years which has generated a significant amount of confusion. We clarify the situation and present a re-naming of the widely studied datasets to avoid future confusion. We also highlight which research papershave dealt with which dataset. Finally, we draw upon our discussion of the literature to present a (non-exhaustive) range of potential future research directions and open issues in exam timetabling research.