KFHE-HOMER: A multi-label ensemble classification algorithm exploiting sensor fusion properties of the Kalman filter

Multi-label classification allows a datapoint to be labelled with more than one class at the same time. In spite of their success in multi-class classification problems, ensemble methods based on approaches other than bagging have not been widely explored for multi-label classification problems. The Kalman Filter-based Heuristic Ensemble (KFHE) is a recent ensemble method that exploits the sensor fusion properties of the Kalman filter to combine several classifier models, and that has been shown to be very effective. This article proposes KFHE-HOMER, an extension of the KFHE ensemble approach to the multi-label domain. KFHE-HOMER sequentially trains multiple HOMER multi-label classifiers and aggregates their outputs using the sensor fusion properties of the Kalman filter. Experiments described in this article show that KFHE-HOMER performs consistently better than existing multi-label methods including existing approaches based on ensembles.Comment: The paper is under consideration at Pattern Recognition Letters, Elsevie

Fables of Faubus?: Testing the Sectoral Shift Hypothesis in the Netherlands Using a Simplified Kalman Filter Model

The presence of structural breaks can seriously affect the outcome of standard regression methods like OLS. Although there are many methods available to deal with them, we focus here on a particular linear filtering method, namely the Kalman Filter. Its results vis a vis a regular OLS approach are illustrated by testing the sectoral shift hypothesis in the Netherlands. Although a rather simplified version of the Kalman Filter is used, it turns out to be a sufficient enough approximation. What we find, is that the variables capturing the sectoral shift hypothesis are the most important in explaining Dutch unemployment behaviour during the postwar period. Thus, the hypo-thesis is endorsed. On the other hand, our highly significant constant term indicates that the inclusion of other variables affecting unemploy-ment may alter the results. Our conclusion thus is a tentative one.labour economics ;

Privacy in Inter-Vehicular Networks: Why simple pseudonym change is not enough

Inter-vehicle communication (IVC) systems disclose rich location information about vehicles. State-of-the-art security architectures are aware of the problem and provide privacy enhancing mechanisms, notably pseudonymous authentication. However, the granularity and the amount of location information IVC protocols divulge, enable an adversary that eavesdrops all traffic throughout an area, to reconstruct long traces of the whereabouts of the majority of vehicles within the same area. Our analysis in this paper confirms the existence of this kind of threat. As a result, it is questionable if strong location privacy is achievable in IVC systems against a powerful adversary.\u

Linear Gaussian Affine Term Structure Models with Unobservable Factors: Calibration and Yield Forecasting

This paper provides a significant numerical evidence for out-of-sample forecasting ability of linear Gaussian interest rate models with unobservable underlying factors. We calibrate one, two and three factor linear Gaussian models using the Kalman filter on two different bond yield data sets and compare their out-of-sample forecasting performance. One step ahead as well as four step ahead out-of-sample forecasts are analyzed based on the weekly data. When evaluating the one step ahead forecasts, it is shown that a one factor model may be adequate when only the short-dated or only the long-dated yields are considered, but two and three factor models performs significantly better when the entire yield spectrum is considered. Furthermore, the results demonstrate that the predictive ability of multi-factor models remains intact far ahead out-of-sample, with accurate predictions available up to one year after the last calibration for one data set and up to three months after the last calibration for the second, more volatile data set. The experimental data denotes two different periods with different yield volatilities, and the stability of model parameters after calibration in both the cases is deemed to be both significant and practically useful. When it comes to four step ahead predictions, the quality of forecasts deteriorates for all models, as can be expected, but the advantage of using a multi-factor model as compared to a one factor model is still significant. In addition to the empirical study above, we also suggest a nonlinear filter based on linear programming for improving the term structure matching at a given point in time. This method, when used in place of a Kalman filter update, improves the term structure fit significantly with a minimal added computational overhead. The improvement achieved with the proposed method is illustrated for out-of-sample data for both the data sets. This method can be used to model a parameterized yield curve consistently with the underlying short rate dynamics

The Applicability of the Sectoral Shift Hypothesis in the Netherlands

The sectoral shift hypothesis in the Netherlands cannot be easily tested for the presence of rigorous structural breaks in the data. Therefore, a Kalman Filter approach is adopted. What we find, is that the variables capturing the sectoral shift hypothesis are the most important in explaining Dutch unemployment behavior during the postwar period. This means that cyclical unemployment in the Netherlands can be viewed as a fluctuation of the natural rate of unemployment.unemployment; sectoral shift hypothesis