13993519
Interval efficiency assessment using data envelopment analysis
This paper studies how to conduct efficiency assessment using data envelopment analysis (DEA) in interval and/or fuzzy input-output environments. A new pair of interval DEA models is constructed on the basis of interval arithmetic, which differs from the existing DEA models handling interval data in that the former is a linear CCR model without the need of extra variable alternations and uses a fixed and unified production frontier (i.e. the same constraint set) to measure the efficiencies of decision-making units (DMUs) with interval input and output data, while the latter is usually a nonlinear optimization problem with the need of extra variable alternations or scale transformations and utilizes variable production frontiers (i.e. different constraint sets) to measure interval efficiencies. Ordinal preference information and fuzzy data are converted into interval data through the estimation of permissible intervals and @a-level sets, respectively, and are incorporated into the interval DEA models. The proposed interval DEA models are developed for measuring the lower and upper bounds of the best relative efficiency of each DMU with interval input and output data, which are different from the interval formed by the worst and the best relative efficiencies of each DMU. A minimax regret-based approach (MRA) is introduced to compare and rank the efficiency intervals of DMUs. Two numerical examples are provided to show the applications of the proposed interval DEA models and the preference ranking approach.
15538833
An approach to avoiding rank reversal in AHP
Analytic hierarchy process (AHP) has been considerably criticized for possible rank reversal phenomenon caused by the addition or deletion of an alternative. This paper looks into the cause of rank reversal phenomenon and finds that rank reversal is caused by change of local priorities before and after an alternative is added or deleted. An approach is therefore proposed to keep the local priorities unchanged to avoid rank reversal phenomenon. Two well-known numerical examples are re-examined using the proposed approach to demonstrate its validity and practicability in rank preservation.
16424206
A general multiple attribute decision-making approach for integrating subjective preferences and objective information
A general multiple attribute decision-making (MADM) approach is proposed to integrate subjective preferences and objective information and to assess the relative weights of attributes, where subjective preferences are expressed either by a fuzzy preference relation on decision alternatives or by a pairwise comparison matrix on the relative weights of attributes or by both of them and objective information is expressed by a decision matrix. Three special cases of the approach are investigated, which are the weighted least deviation norm (WLDN), the weighted least-square deviation norm (WLSDN) and the weighted minimax deviation norm (WMDN) approaches. The extension of the general approach to group decision-making and the choice of decision parameters are also discussed. A numerical example is examined to show the applications of the proposed MADM approaches.
46223238
Environmental impact assessment using the evidential reasoning approach
Environmental impact assessment (EIA) problems are often characterised by a large number of identified environmental factors that are qualitative in nature and can only be assessed on the basis of human judgments, which inevitably involve various types of uncertainties such as ignorance and fuzziness. So, EIA problems need to be modelled and analysed using methods that can handle uncertainties. The evidential reasoning (ER) approach provides such a modelling framework and analysis method. In this paper the ER approach will be applied to conduct EIA analysis for the first time. The environmental impact consequences are characterized by a set of assessment grades that are assumed to be collectively exhaustive and mutually exclusive. All assessment information, quantitative or qualitative, complete or incomplete, and precise or imprecise, is modelled using a unified framework of a belief structure. The original ER approach with a recursive ER algorithm will be introduced and a new analytical ER algorithm will be investigated which provides a means for using the ER approach in decision situations where an explicit ER aggregation function is needed such as in optimisation problems. The ER approach will be used to aggregate multiple environmental factors, resulting in an aggregated distributed assessment for each alternative policy. A numerical example and its modified version are studied to illustrate the detailed implementation process of the ER approach and demonstrate its potential applications in EIA.
19364030
The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees
Abstract Multiple attribute decision analysis (MADA) problems having both quantitative and qualitative attributes under uncertainty can be modeled using the evidential reasoning (ER) approach. Several types of uncertainties such as ignorance and fuzziness can be modeled in the ER framework. In this paper, the ER approach will be extended to model new types of uncertainties including interval belief degrees and interval data that could be incurred in decision situations such as group decision making. The Dempster–Shafer (D–S) theory of evidence is first extended, which is one of the bases of the ER approach. The analytical ER algorithm is used to combine all evidence simultaneously. Two pairs of nonlinear optimization models are constructed to estimate the upper and lower bounds of the combined belief degrees and to compute the maximum and the minimum expected utilities of each alternative, respectively. Interval data are equivalently transformed to interval belief degrees and are incorporated into the nonlinear optimization models. A cargo ship selection problem is examined to show the implementation process of the proposed approach.
18488788
A preference aggregation method through the estimation of utility intervals
In this paper, a preference aggregation method is developed for ranking alternative courses of actions by combining preference rankings of alternatives given on individual criteria or by individual decision makers. In the method, preference rankings are viewed as constraints on alternative utilities, which are normalized, and linear programming models are constructed to estimate utility intervals, which are weighted and averaged to generate an aggregated utility interval. A simple yet pragmatic interval ranking method is used to compare and/or rank alternatives. The final ranking is generated as the most likely ranking with certain degrees of belief. Three numerical examples are examined to illustrate the potential applications of the proposed method.
7211983
Theoretical Analysis for Communication-Induced Checkpointing Protocols with Rollback-Dependency Trackability
Rollback-Dependency Trackability (RDT) is a property that states that all rollback dependencies between local checkpoints are on-line trackable by using a transitive dependency vector. In this paper, we address three fundamental issues in the design of communication-induced checkpointing protocols that ensure RDT. First, we prove that the following intuition commonly assumed in the literature is in fact false: If a protocol forces a checkpoint only at a stronger condition, then it must take, at most, as many forced checkpoints as a protocol based on a weaker condition. This result implies that the common approach of sharpening the checkpoint-inducing condition by piggybacking more control information on each message may not always yield a more efficient protocol. Next, we prove that there is no optimal on-line RDT protocol that takes fewer forced checkpoints than any other RDT protocol for all possible communication patterns. Finally, since comparing checkpoint-inducing conditions is not sufficient for comparing protocol performance, we present some formal techniques for comparing the performance of several existing RDT protocols.
206873031
Lossy compression of images using logic minimization
A technique for lossy compression of images is presented, utilizing ideas of logic minimization. The approach specifically addresses the compression of the binary image data originated in block truncation coding (BTC). The binary vector corresponding to a block of 4/spl times/4 pixels is treated as the output of a Boolean function and prime cubes are generated. The largest prime cube is encoded. Bit rate less than 1.5 bits/pel is attained in BTC without many perceivable errors in the reconstructed grey scale image. Training, pre-stored tables or codebooks, and prior knowledge of the image source are not required by the technique which uses simple logic operations. Computational simplicity of the algorithm makes it suitable for VLSI implementation. Potential of the technique in attaining a rate less than 0.5 bit/pel by applying it on image sequences and extending to blocks of larger size is indicated.
2488079
Web-Based Unsupervised Learning for Query Formulation in Question Answering
Converting questions to effective queries is crucial to open-domain question answering systems. In this paper, we present a web-based unsupervised learning approach for transforming a given natural-language question to an effective query. The method involves querying a search engine for Web passages that contain the answer to the question, extracting patterns that characterize fine-grained classification for answers, and linking these patterns with n-grams in answer passages. Independent evaluation on a set of questions shows that the proposed approach outperforms a naive keyword-based approach in terms of mean reciprocal rank and human effort.
8204880
Privacy aware data generation for testing database applications
Testing of database applications is of great importance. A significant issue in database application testing consists in the availability of representative data. In this paper, we investigate the problem of generating a synthetic database based on a-priori knowledge about a production database. Our approach is to fit general location model using various characteristics (e.g., constraints, statistics, rules) extracted from the production database and then generate the synthetic data using model learnt. The generated data is valid and similar to real data in terms of statistical distribution, hence it can be used for functional and performance testing. As characteristics extracted may contain information which may be used by attacker to derive some confidential information about individuals, we present our disclosure analysis method which applies cell suppression technique for identity disclosure analysis and perturbation for value disclosure.
9203132
Nonlinear Estimation and Modeling of fMRI Data Using Spatio-temporal Support Vector Regression
This paper presents a new and general nonlinear framework for fMRI data analysis based on statistical learning methodology: support vector machines. Unlike most current methods which assume a linear model for simplicity, the estimation and analysis of fMRI signal within the proposed framework is nonlinear, which matches recent findings on the dynamics underlying neural activity and hemodynamic physiology. The approach utilizes spatio-temporal support vector regression (SVR), within which the intrinsic spatio-temporal autocorrelations in fMRI data are reflected. The novel formulation of the problem allows merging model-driven with data-driven methods, and therefore unifies these two currently separate modes of fMRI analysis. In addition, multiresolution signal analysis is achieved and developed. Other advantages of the approach are: avoidance of interpolation after motion estimation, embedded removal of low-frequency noise components, and easy incorporation of multi-run, multi-subject, and multi-task studies into the framework.