A Variable Metric Probabilistic k-Nearest-Neighbours Classifier

Copyright © 2004 Springer Verlag. The final publication is available at link.springer.com5th International Conference, Exeter, UK. August 25-27, 2004. ProceedingsBook title: Intelligent Data Engineering and Automated Learning – IDEAL 2004k-nearest neighbour (k-nn) model is a simple, popular classifier. Probabilistic k-nn is a more powerful variant in which the model is cast in a Bayesian framework using (reversible jump) Markov chain Monte Carlo methods to average out the uncertainy over the model parameters.The k-nn classifier depends crucially on the metric used to determine distances between data points. However, scalings between features, and indeed whether some subset of features is redundant, are seldom known a priori. Here we introduce a variable metric extension to the probabilistic k-nn classifier, which permits averaging over all rotations and scalings of the data. In addition, the method permits automatic rejection of irrelevant features. Examples are provided on synthetic data, illustrating how the method can deform feature space and select salient features, and also on real-world data

Fast k-NN classifier for documents based on a graph structure

In this paper, a fast k nearest neighbors (k-NN) classifier for documents is presented. Documents are usually represented in a high-dimensional feature space, where terms appeared on it are treated as features and the weight of each term reflects its importance in the document. There are many approaches to find the vicinity of an object, but their performance drastically decreases as the number of dimensions grows. This problem prevents its application for documents. The proposed method is based on a graph index structure with a fast search algorithm. It’s high selectivity permits to obtain a similar classification quality than exhaustive classifier, with a few number of computed distances. Our experimental results show that it is feasible the use of the proposed method in problems of very high dimensionality, such as Text Mining

Screened Coulomb interactions in metallic alloys: I. Universal screening in the atomic sphere approximation

We have used the locally self-consistent Green's function (LSGF) method in supercell calculations to establish the distribution of the net charges assigned to the atomic spheres of the alloy components in metallic alloys with different compositions and degrees of order. This allows us to determine the Madelung potential energy of a random alloy in the single-site mean field approximation which makes the conventional single-site density-functional- theory coherent potential approximation (SS-DFT-CPA) method practically identical to the supercell LSGF method with a single-site local interaction zone that yields an exact solution of the DFT problem. We demonstrate that the basic mechanism which governs the charge distribution is the screening of the net charges of the alloy components that makes the direct Coulomb interactions short-ranged. In the atomic sphere approximation, this screening appears to be almost independent of the alloy composition, lattice spacing, and crystal structure. A formalism which allows a consistent treatment of the screened Coulomb interactions within the single-site mean-filed approximation is outlined. We also derive the contribution of the screened Coulomb interactions to the S2 formalism and the generalized perturbation method.Comment: 28 pages, 8 figure

Morbidity after sentinel lymph node biopsy in primary breast cancer: results from a randomized controlled trial

Purpose: Axillary lymph node dissection (ALND) as part of surgical treatment for patients with breast cancer is associated with significant morbidity. Sentinel lymph node biopsy (SLNB) is a newly developed method of staging the axilla and has the potential to avoid an ALND in lymph node–negative patients, thereby minimizing morbidity. The aim of this study was to investigate physical and psychological morbidity after SLNB in the treatment of early breast cancer in a randomized controlled trial.<p></p> Patients and Methods: Between November 1999 and February 2003, 298 patients with early breast cancer (tumors 3 cm or less on ultrasound examination) who were clinically node negative were randomly allocated to undergo ALND (control group) or SLNB followed by ALND if subsequently found to be lymph node positive (study group). A detailed assessment of physical and psychological morbidity was performed during a 1-year period postoperatively.<p></p> Results: A significant reduction in postoperative arm swelling, rate of seroma formation, numbness, loss of sensitivity to light touch and pinprick was observed in the study group. Although shoulder mobility was less impaired on average in the study group, this was significant only for abduction at 1 month and flexion at 3 months. Scores reflecting quality of life and psychological morbidity were significantly better in the study group in the immediate postoperative period, with fewer long-term differences.<p></p> Conclusion: SLNB in patients undergoing surgery for breast cancer results in a significant reduction in physical and psychological morbidity.<p></p&gt

Probability Based Metrics for Nearest Neighbor Classification and Case-Based Reasoning

. This paper is focused on a class of metrics for the Nearest Neighbor classifier, whose definition is based on statistics computed on the case base. We show that these metrics basically rely on a probability estimation phase. In particular, we reconsider a metric proposed in the 80's by Short and Fukunaga, we extend its definition to an input space that includes categorical features and we evaluate empirically its performance. Moreover, we present an original probability based metric, called Minimum Risk Metric (MRM), i.e. a metric for classification tasks that exploits estimates of the posterior probabilities. MRM is optimal, in the sense that it optimizes the finite misclassification risk, whereas the Short and Fukunaga Metric minimize the difference between finite risk and asymptotic risk. An experimental comparison of MRM with the Short and Fukunaga Metric, the Value Difference Metric, and Euclidean-- Hamming metrics on benchmark datasets shows that MRM outperforms the other metri..

Microvolt T-wave alternans (MTWA) testing in 'real world' heart failure (HF): a study of prevalence and incremental prognostic value

Background Ventricular arrhythmias contribute to the high risk of death in heart failure (HF) and can be treated with an implantable cardioverter-defibrillator (ICD). Microvolt T-wave alternans (MTWA) testing examines beat-to-beat fluctuations in the morphology of the T-wave. Alternans is believed to reflect dynamic instability of repolarisation and to be linked, mechanistically, to ventricular arrhythmias. Observational studies in highly selected populations have suggested that MTWA testing may identify individuals likely to benefit from a primary prevention ICD. The aims of this study were to evaluate the applicability of MTWA testing in an unselected cohort of patients recently hospitalised with HF and determine the prevalence and incremental prognostic value of an abnormal test.<p></p> Methods Consecutive admissions with confirmed HF (typical clinical findings and BNP>100 pg/ml) were recruited in three hospitals from 1 December 2006 to 12 January 2009. Survivors were invited to attend 1-month post-discharge for MTWA testing (HearTWave II, Cambridge Heart).<p></p> Results 648 of 1003 patients recruited returned for MTWA testing (58% males, mean age 70.8 years). 318 patients (49%) were ineligible for MTWA testing due to atrial fibrillation (AF), pacemaker-dependency or inability to exercise. Of the 330 patients who underwent MTWA treadmill testing, 100 (30%) were positive, 78 (24%) were negative and 152 (46%) were indeterminate. Failure to achieve the target heart rate due to chronotropic incompetence, secondary to β-blocker therapy or physical limitations, accounted for 75% of indeterminate tests. 131 deaths occurred during a mean follow-up of 18 months. 23% of ineligible patients died vs 17% of eligible patients. 12%, 20% and 19% of patients with a positive, negative and indeterminate test, respectively, died (p=0.24). MTWA results were analysed in the accepted way of non-negative (positive and indeterminate) and negative, but there was still no difference in mortality between the groups (p=0.39). MTWA showed no incremental prognostic value in a multivariable mortality model. The independent predictors of mortality were: lower body mass index (HR 0.96 [95% CI 0.93 to 0.99], p=0.01), New York Heart Association class III–IV (1.72 [95% CI 1.2 to 2.47], p=0.003), previous myocardial infarction (1.68 [95% CI 1.18 to 2.4], p=0.004), elevated B-type natriuretic peptide concentration (1.36 [95% CI 1.12 to 1.65], p=0.002) and elevated troponin (1.57 [95% CI 1.04 to 2.37], p=0.03).<p></p> Conclusion MTWA treadmill-testing was not widely applicable in typical patients with HF and failed to predict mortality risk. At present MTWA cannot be endorsed as a tool for improving risk stratification in HF.<p></p&gt