Optimal Route Search with the Coverage of Users' Preferences

The preferences of users are important in route search and planning. For example, when a user plans a trip within a city, their preferences can be expressed as keywords shopping mall, restaurant, and museum, with weights 0.5, 0.4, and 0.1, respectively. The resulting route should best satisfy their weighted preferences. In this paper, we take into account the weighted user preferences in route search, and present a keyword coverage problem, which finds an optimal route from a source location to a target location such that the keyword coverage is optimized and that the budget score satisfies a specified constraint. We prove that this problem is NP-hard. To solve this complex problem, we pro- pose an optimal route search based on an A* variant for which we have defined an admissible heuristic function. The experiments conducted on real-world datasets demonstrate both the efficiency and accu- racy of our proposed algorithms.

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.

A sequential algorithm for sparse support vector classifiers

Support vector machines (SVMs), though accurate, are not preferred in applications requiring high classification speed or when deployed in systems of limited computational resources, due to the large number of support vectors involved in the model. To overcome this problem we have devised a primal SVM method with the following properties: (1) it solves for the SVM representation without the need to invoke the representer theorem, (2) forward and backward selections are combined to approach the final globally optimal solution, and (3) a criterion is introduced for identification of support vectors leading to a much reduced support vector set. In addition to introducing this method the paper analyzes the complexity of the algorithm and presents test results on three public benchmark problems and a human activity recognition application. These applications demonstrate the effectiveness and efficiency of the proposed algorithm.


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Optimal Front Page Advertising

We consider a model of an on-line software market, where an intermediary distributes products from sellers to buyers. When products of sellers are vertically differentiated, an intermediary, earning a proportion of sales, has an incentive to hide the worse product on the second page, and only keep the better product on the front page: that weakens the competition, allowing the seller with the better product to charge a higher price. With heterogeneous visiting costs to the second page, the platform's revenue might improve, but the outcome will become socially suboptimal.

Collating the knowledge base for core outcome set development: developing and appraising the search strategy for a systematic review

Background: The COMET (Core Outcome Measures in Effectiveness Trials) Initiative is developing a publicly accessible online resource to collate the knowledge base for core outcome set development (COS) and the applied work from different health conditions. Ensuring that the database is as comprehensive as possible and keeping it up to date are key to its value for users. This requires the development and application of an optimal, multi-faceted search strategy to identify relevant material. This paper describes the challenges of designing and implementing such a search, outlining the development of the search strategy for studies of COS development, and, in turn, the process for establishing a database of COS.

Methods: We investigated the performance characteristics of this strategy including sensitivity, precision and numbers needed to read. We compared the contribution of databases towards identifying included studies to identify the best combination of methods to retrieve all included studies.

Results: Recall of the search strategies ranged from 4% to 87%, and precision from 0.77% to 1.13%. MEDLINE performed best in terms of recall, retrieving 216 (87%) of the 250 included records, followed by Scopus (44%). The Cochrane Methodology Register found just 4% of the included records. MEDLINE was also the database with the highest precision. The number needed to read varied between 89 (MEDLINE) and 130 (SCOPUS).

Conclusions: We found that two databases and hand searching were required to locate all of the studies in this review. MEDLINE alone retrieved 87% of the included studies, but actually 97% of the included studies were indexed on MEDLINE. The Cochrane Methodology Register did not contribute any records that were not found in the other databases, and will not be included in our future searches to identify studies developing COS. SCOPUS had the lowest precision rate (0.77) and highest number needed to read (130). In future COMET searches for COS a balance needs to be struck between the work involved in screening large numbers of records, the frequency of the searching and the likelihood that eligible studies will be identified by means other than the database searches.

Sequential treatment with flavopiridol synergistically enhances pyrrolo-1,5-benzoxazepine-induced apoptosis in human chronic myeloid leukaemia cells including those resistant to imatinib treatment

The Bcr-Abl kinase inhibitor, imatinib mesylate, is the front line treatment for chronic myeloid leukaemia (CML), but the emergence of imatinib resistance has led to the search for alternative drug treatments and the examination of combination therapies to overcome imatinib resistance. The pro-apoptotic PBOX compounds are a recently developed novel series of microtubule targeting agents (MTAs) that depolymerise tubulin. Recent data demonstrating enhanced MTA-induced tumour cell apoptosis upon combination with the cyclin dependent kinase (CDK)-1 inhibitor flavopiridol prompted us to examine whether this compound could similarly enhance the effect of the PBOX compounds. We thus characterised the apoptotic and cell cycle events associated with combination therapy of the PBOX compounds and flavopiridol and results showed a sequence dependent, synergistic enhancement of apoptosis in CML cells including those expressing the imatinib-resistant T315I mutant. Flavopiridol reduced the number of polyploid cells formed in response to PBOX treatment but only to a small extent, suggesting that inhibition of endoreplication was unlikely to play a major role in the mechanism by which flavopiridol synergistically enhanced PBOX-induced apoptosis. The addition of flavopiridol following PBOX-6 treatment did however result in an accelerated exit from the G2/M transition accompanied by an enhanced downregulation and deactivation of the CDK1/cyclin B1 complex and an enhanced degradation of the inhibitor of apoptosis protein (IAP) survivin. In conclusion, results from this study highlight the potential of these novel series of PBOX compounds, alone or in sequential combination with flavopiridol, as an effective therapy against CML.

Search filters: mapping the literature using a novel form of content analysis.

Background: Search filters are combinations of words and phrases designed to retrieve an optimal set of records on a particular topic (subject filters) or study design (methodological filters). Information specialists are increasingly turning to reusable filters to focus their searches. However, the extent of the academic literature on search filters is unknown. We provide a broad overview to the academic literature on search filters.
Objectives: To map the academic literature on search filters from 2004 to 2015 using a novel form of content analysis.
Methods: We conducted a comprehensive search for literature between 2004 and 2015 across eight databases using a subjectively derived search strategy. We identified key words from titles, grouped them into categories, and examined their frequency and co-occurrences.
Results: The majority of records were housed in Embase (n = 178) and MEDLINE (n = 154). Over the last decade, both databases appeared to exhibit a bimodal distribution with the number of publications on search filters rising until 2006, before dipping in 2007, and steadily increasing until 2012. Few articles appeared in social science databases over the same time frame (e.g. Social Services Abstracts, n = 3).
Unsurprisingly, the term ‘search’ appeared in most titles, and quite often, was used as a noun adjunct for the word 'filter' and ‘strategy’. Across the papers, the purpose of searches as a means of 'identifying' information and gathering ‘evidence’ from 'databases' emerged quite strongly. Other terms relating to the methodological assessment of search filters, such as precision and validation, also appeared albeit less frequently.
Conclusions: Our findings show surprising commonality across the papers with regard to the literature on search filters. Much of the literature seems to be focused on developing search filters to identify and retrieve information, as opposed to testing or validating such filters. Furthermore, the literature is mostly housed in health-related databases, namely MEDLINE, CINAHL, and Embase, implying that it is medically driven. Relatively few papers focus on the use of search filters in the social sciences.

Learning Bayesian Networks with Bounded Tree-Width via Guided Search

Bounding the tree-width of a Bayesian network can reduce the chance of overfitting, and allows exact inference to be performed efficiently. Several existing algorithms tackle the problem of learning bounded tree-width Bayesian networks by learning from k-trees as super-structures, but they do not scale to large domains and/or large tree-width. We propose a guided search algorithm to find k-trees with maximum Informative scores, which is a measure of quality for the k-tree in yielding good Bayesian networks. The algorithm achieves close to optimal performance compared to exact solutions in small domains, and can discover better networks than existing approximate methods can in large domains. It also provides an optimal elimination order of variables that guarantees small complexity for later runs of exact inference. Comparisons with well-known approaches in terms of learning and inference accuracy illustrate its capabilities.