From ranking fuzzy numbers to solving fuzzy linear programming: a comprehensive review

Solving fuzzy linear programming (FLP) requires the employment of a consistent ranking of fuzzy numbers. Ineffective fuzzy number ranking would lead to a flawed and erroneous solving approach. This paper presents a comprehensive and extensive review on fuzzy number ranking methods. Ranking techniques are categorised into six classes based on their characteristics. They include centroid methods, distance methods, area methods, lexicographical methods, methods based on decision maker's viewpoint, and methods based on left and right spreads. A survey on solving approaches to FLP is also reported. We then point out errors in several existing methods that are relevant to the ranking of fuzzy numbers and thence suggest an effective method to solve FLP. Consequently, FLP problems are converted into non-fuzzy single (or multiple) objective linear programming based on a consistent centroid-based ranking of fuzzy numbers. Solutions of FLP are then obtained by solving corresponding crisp single (or multiple) objective programming problems by conventional methods.

Solving fuzzy programming with a consistent fuzzy number ranking

 Some illustrative examples are provided to identify the ineffective and unrealistic characteristics of existing approaches to solving fuzzy linear programming (FLP) problems (with single or multiple objectives). We point out the error in existing methods concerning the ranking of fuzzy numbers and thence suggest an effective method to solve the FLP. Based on the consistent centroid-based ranking of fuzzy numbers, the FLP problems are transformed into non-fuzzy single (or multiple) objective linear programming. Solutions of FLP are then crisp single or multiple objective programming problems, which can respectively be obtained by conventional methods.

A noisy-smoothing relevance feedback method for content-based medical image retrieval

In this paper, we address a new problem of noisy images which present in the procedure of relevance feedback for medical image retrieval. We concentrate on the noisy images, caused by the users mislabeling some irrelevant images as relevant ones, and a noisy-smoothing relevance feedback (NS-RF) method is proposed. In NS-RF, a two-step strategy is proposed to handle the noisy images. In step 1, a noisy elimination algorithm is adopted to identify and eliminate the noisy images. In step 2, to further alleviate the influence of noisy images, a fuzzy membership function is employed to estimate the relevance probabilities of retained relevant images. After noisy handling, the fuzzy support vector machine, which can take into account different relevant images with different relevance probabilities, is adopted to re-rank the images. The experimental results on the IRMA medical image collection demonstrate that the proposed method can deal with the noisy images effectively.

Relevance of traditional management accounting in the public sector

 The major reforms that the Australian public sector went through in the early 1980s were characterised by a stress on efficiency and effectiveness. The reforms led to the import of a private sector mangement philosophy to the public sector, including private sector based management accounting techniques. It is this import that motivated the critical theoretical discussion presented in this paper concerning the applicability of traditional private sector management accounting techniques in the public sector. The paper sets out the pros and cons of such application and provides directions for future research.

The relevance of Asian development bank: existing in the shadow of the Asian infrastructure investment bank

This paper analyses the potential impact of the China-led Asian Infrastructure Investment Bank (AIIB) on the Japan-USA-led Asian Development Bank (ADB). Given the financial strengths and the technical know-how of the newly formed AIIB there is a question about thefuture role and indeed relevance of the ADB. The questions canvassed in this article refer to ADB’s ability to change and adapt to the new situation, where it is no longer the dominant multi-lateral development bank (MDB) in the Asia-Pacific region. Against this background the discussion turns to issues concerning the geo-political sphere of influence of the ADB andAIIB and analyses the ADB – AIIB geo-political equilibrium in the Asia-Pacific region. Subsequently this paper discusses factors that may impact on ADB’s future relevance.

Evaluation of three field monitoring-density estimation protocols and their relevance to Komodo dragon conservation

Finding practical ways to robustly estimate abundance or density trends in threatened species is a key facet for effective conservation management. Further identifying less expensive monitoring methods that provide adequate data for robust population density estimates can facilitate increased investment into other conservation initiatives needed for species recovery. Here we evaluated and compared inference-and cost-effectiveness criteria for three field monitoring-density estimation protocols to improve conservation activities for the threatened Komodo dragon (Varanus komodoensis). We undertook line-transect counts, cage trapping and camera monitoring surveys for Komodo dragons at 11 sites within protected areas in Eastern Indonesia to collect data to estimate density using distance sampling methods or the Royle-Nichols abundance induced heterogeneity model. Distance sampling estimates were considered poor due to large confidence intervals, a high coefficient of variation and that false absences were obtained in 45 % of sites where other monitoring methods detected lizards present. The Royle-Nichols model using presence/absence data obtained from cage trapping and camera monitoring produced highly correlated density estimates, obtained similar measures of precision and recorded no false absences in data collation. However because costs associated with camera monitoring were considerably less than cage trapping methods, albeit marginally more expensive than distance sampling, better inference from this method is advocated for ongoing population monitoring of Komodo dragons. Further the cost-savings achieved by adopting this field monitoring method could facilitate increased expenditure on alternative management strategies that could help address current declines in two Komodo dragon populations.

Modelling human preferences for ranking and collaborative filtering: a probabilistic ordered partition approach

Learning preference models from human generated data is an important task in modern information processing systems. Its popular setting consists of simple input ratings, assigned with numerical values to indicate their relevancy with respect to a specific query. Since ratings are often specified within a small range, several objects may have the same ratings, thus creating ties among objects for a given query. Dealing with this phenomena presents a general problem of modelling preferences in the presence of ties and being query-specific. To this end, we present in this paper a novel approach by constructing probabilistic models directly on the collection of objects exploiting the combinatorial structure induced by the ties among them. The proposed probabilistic setting allows exploration of a super-exponential combinatorial state-space with unknown numbers of partitions and unknown order among them. Learning and inference in such a large state-space are challenging, and yet we present in this paper efficient algorithms to perform these tasks. Our approach exploits discrete choice theory, imposing generative process such that the finite set of objects is partitioned into subsets in a stagewise procedure, and thus reducing the state-space at each stage significantly. Efficient Markov chain Monte Carlo algorithms are then presented for the proposed models. We demonstrate that the model can potentially be trained in a large-scale setting of hundreds of thousands objects using an ordinary computer. In fact, in some special cases with appropriate model specification, our models can be learned in linear time. We evaluate the models on two application areas: (i) document ranking with the data from the Yahoo! challenge and (ii) collaborative filtering with movie data. We demonstrate that the models are competitive against state-of-the-arts.