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.

Modelling attribute non-attendance in choice experiments for rural landscape valuation

Non-market effects of agriculture are often estimated using discrete choice models from stated preference surveys. In this context we propose two ways of modelling attribute non-attendance. The first involves constraining coefficients to zero in a latent class framework, whereas the second is based on stochastic attribute selection and grounded in Bayesian estimation. Their implications are explored in the context of a stated preference survey designed to value landscapes in Ireland. Taking account of attribute non-attendance with these data improves fit and tends to involve two attributes one of which is likely to be cost, thereby leading to substantive changes in derived welfare estimates.

A Holistic vs. an Attribute-based Approach to Agri-Environmental Policy Valuation: Do Welfare Estimates Differ?

Different economic valuation methodologies can be used to value the non-market benefits of an agri-environmental scheme. In particular, the non-market value can be examined by assessing the public's willingness to pay for the policy outputs as a whole or by modelling the preferences of society for the component attributes of the rural landscape that result from the implementation of the policy. In this article we examine whether the welfare values estimated for an agri-environmental policy are significantly different between an holistic valuation methodology (using contingent valuation) and an attribute-based valuation methodology (choice experiment). It is argued that the valuation methodology chosen should be based on whether or not the overall objective is the valuation of the agri-environment policy package in its entirety or the valuation of each of the policy's distinct environmental outputs.

Managing SMT Resource Usage through Speculative Instruction Window Weighting

Simultaneous multithreading processors dynamically share processor resources between multiple threads. In general, shared SMT resources may be managed explicitly, for instance, by dynamically setting queue occupation bounds for each thread as in the DCRA and Hill-Climbing policies. Alternatively, resources may be managed implicitly; that is, resource usage is controlled by placing the desired instruction mix in the resources. In this case, the main resource management tool is the instruction fetch policy which must predict the behavior of each thread (branch mispredictions, long-latency loads, etc.) as it fetches instructions.

Fetch Gating Control through Speculative Instruction Window Weighting

In a dynamic reordering superscalar processor, the front-end fetches instructions and places them in the issue queue. Instructions are then issued by the back-end execution core. Till recently, the front-end was designed to maximize performance without considering energy consumption. The front-end fetches instructions as fast as it can until it is stalled by a filled issue queue or some other blocking structure. This approach wastes energy: (i) speculative execution causes many wrong-path instructions to be fetched and executed, and (ii) back-end execution rate is usually less than its peak rate, but front-end structures are dimensioned to sustained peak performance. Dynamically reducing the front-end instruction rate and the active size of front-end structure (e.g. issue queue) is a required performance-energy trade-off. Techniques proposed in the literature attack only one of these effects.
In previous work, we have proposed Speculative Instruction Window Weighting (SIWW) [21], a fetch gating technique that allows to address both fetch gating and instruction issue queue dynamic sizing. SIWW computes a global weight on the set of inflight instructions. This weight depends on the number and types of inflight instructions (non-branches, high confidence or low confidence branches, ...). The front-end instruction rate can be continuously adapted based on this weight. This paper extends the analysis of SIWW performed in previous work. It shows that SIWW performs better than previously proposed fetch gating techniques and that SIWW allows to dynamically adapt the size of the active instruction queue.

Multi-Attribute SCADA-Specific Intrusion Detection System for Power Networks

The increased interconnectivity and complexity of supervisory control and data acquisition (SCADA) systems in power system networks has exposed the systems to a multitude of potential vulnerabilities. In this paper, we present a novel approach for a next-generation SCADA-specific intrusion detection system (IDS). The proposed system analyzes multiple attributes in order to provide a comprehensive solution that is able to mitigate varied cyber-attack threats. The multiattribute IDS comprises a heterogeneous white list and behavior-based concept in order to make SCADA cybersystems more secure. This paper also proposes a multilayer cyber-security framework based on IDS for protecting SCADA cybersecurity in smart grids without compromising the availability of normal data. In addition, this paper presents a SCADA-specific cybersecurity testbed to investigate simulated attacks, which has been used in this paper to validate the proposed approach.

It's not that I don't care, I just don't care very much: confounding between attribute non-attendance and taste heterogeneity

With the growing interest in the topic of attribute non-attendance, there is now widespread use of latent class (LC) structures aimed at capturing such behaviour, across a number of different fields. Specifically, these studies rely on a confirmatory LC model, using two separate values for each coefficient, one of which is fixed to zero while the other is estimated, and then use the obtained class probabilities as an indication of the degree of attribute non-attendance. In the present paper, we argue that this approach is in fact misguided, and that the results are likely to be affected by confounding with regular taste heterogeneity. We contrast the confirmatory model with an exploratory LC structure in which the values in both classes are estimated. We also put forward a combined latent class mixed logit model (LC-MMNL) which allows jointly for attribute non-attendance and for continuous taste heterogeneity. Across three separate case studies, the exploratory LC model clearly rejects the confirmatory LC approach and suggests that rates of non-attendance may be much lower than what is suggested by the standard model, or even zero. The combined LC-MMNL model similarly produces significant improvements in model fit, along with substantial reductions in the implied rate of attribute non-attendance, in some cases even eliminating the phenomena across the sample population. Our results thus call for a reappraisal of the large body of recent work that has implied high rates of attribute non-attendance for some attributes. Finally, we also highlight a number of general issues with attribute non-attendance, in particular relating to the computation of willingness to pay measures.