Applications of inductive learning algorithms to realworld data mining problems have shown repeatedly that using accuracy to compare classifiers is not adequate because the underlying assumptions rarely hold. We present a method for the comparison of classifier performance that is robust to imprecise class distributions and misclassification costs. The ROC convex hull method combines techniques from ROC analysis, decision analysis and computational geometry, and adapts them to the particulars of analyzing learned classifiers. The method is efficient and incremental, minimizes the management of classifier performance data, and allows for clear visual comparisons and sensitivity analyses. Introduction When mining data with inductive methods, we often experiment with a wide variety of learning algorithms, using different algorithm parameters, varying output threshold values, and using different training regimens. Such experimentation yields a large number of classifiers to be evaluated a...

In real-world environments it is usually difficult to specify target operating conditions precisely. This uncertainty makes building robust classification systems problematic. We show that it is possible to build a hybrid classifier that will perform at least as well as the best available classifier for any target conditions. This robust performance extends across a wide variety of comparison frameworks, including the optimization of metrics such as accuracy, expected cost, lift, precision, recall, and workforce utilization. In some cases, the performance of the hybrid can actually surpass that of the best known classifier. The hybrid is also efficient to build, to store, and to update. Finally, we provide empirical evidence that a robust hybrid classifier is needed for many real-world problems. Introduction Traditionally, classification systems have been built by experimenting with many different classifiers, comparing their performance and choosing the classifier that performs best....

We have carried out a study on a collection of elec-- irocardiograms from patients who suffer from congestive heart failure, heart-transplant patients, and normal subjects, using wavelet-based multiresolution techniques and receiver-operating-characteristic (ROC) analysis. The scale-dependent wavelet-coeficient standard deviation OW,V(m) is found to be superior to two commonly used heart-rate-variability me~ures for diagnosing cardiac dysfunction, the interbeat-interval standard deviation ~nt and the spectral scaling exponent 6. A recent Israeli-Danish study of diabetic patients that confirms our oh-‘ \ servations is discussed. I.

The interaction of TCRs with MHC peptide ligands can be highly flexible, so that many different peptides are recognized by the same TCR in the context of a single restriction element. We provide a quantitative description of such interactions, which allows the identification of T cell epitopes and molecular mimics. The response of T cell clones to positional scanning synthetic combinatorial libraries is analyzed with a mathematical approach that is based on a model of independent contribution of individual amino acids to peptide Ag recognition. This biometric analysis compares the information derived from these libraries composed of trillions of decapeptides with all the millions of decapeptides contained in a protein database to rank and predict the most stimulatory peptides for a given T cell clone. We demonstrate the predictive power of the novel strategy and show that, together with gene expression profiling by cDNA microarrays, it leads to the identification of novel candidate autoantigens in the inflammatory autoimmune disease, multiple sclerosis. The Journal of Immunology, 2001, 167: 2130–2141. The CD8 � and CD4 � T lymphocytes recognize short peptides of 8–10 and 12–16 aa in the context of self MHC class I and class II molecules, respectively (1, 2). During the last 15 years, this central process of cellular immune responses has received enormous attention and has been dissected using a vast array of different immunological and biochemical techniques.

In this paper, we examine the use of machine learning to improve a rooftop detection process, which is one step in a vision system that recognizes buildings in overhead imagery. We review the problem of analyzing aerial images and describe an existing vision system that automates the recognition of buildings in such images. After this, we briefly review two well-known learning algorithms, representing different inductive biases, that we selected to improve rooftop detection. An important aspect of this problem is that the data sets are highly skewed and the cost of mistakes differs for the two classes, so we evaluate the algorithms under varying misclassification costs using ROC analysis. We report three sets of experiments designed to illuminate facets of applying machine learning to the image analysis task. One set of studies focuses on within-image learning, in which both training and testing data are derived from the same image. Another addresses between-image learning, in which tr...

Applications include diagnosis, prognosis, & treatment optimization, often thru biomarker discovery Copyright 2008 © Limsoon Wong 1 What is Knowledge Discovery? Jonathan’s blocks Jessica’s blocks Whose block is this? Jonathan’s rules Jessica’s rules: Blue or Circle: All the rest

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This study had three objectives: (1) to assess the validity of questionnaire-based measures in identifying households experiencing hunger and food insecurity, (2) to examine the interrelationships of different questionnaire-based measures, and (3) to examine the construction of a continuous food insecurity scale intended to differentiate three levels of food insecurity within households. A 1993 survey of 193 randomly sampled rural households with women and children living at home provided data on demographics, risk factors for food insecurity, Radimer/Cornell, CCHIP, and NHANES III hunger and food insecurity items, coping strategies, fruit and vegetable consumption, disordered eating behaviors, height, weight, dietary recall, and household food-stores inventory. This information was used to develop a definitive criterion measure for hunger and food insecurity, against which the Radimer/Cornell and CCHIP questionnaire-based measures, the NHANES III item, and the continuous food insecurity scale were tested for their specificity and sensitivity in measuring levels of food insecurity. Estimating the Prevalence of Hunger and Food Insecurity: