Supervised Classification Analysis

Clustering is a highly effective technique in data analysis. Prior to the introduction of clustering, it is important to understand the difference between clustering (imsuper-vised classification) and supervised classification. In supervised classification, we use traming objects with known class labels to develop a model and then deploy... 

Lasch and cowoikers describe in Chap. 8 their group s efforts to improve taxonomic resolution without compromising the simplicity and the speed of MALDI TOF MS. Such improvements may be achieved by signature database expansion with novel and diverse strains, optimization, and standardization of sample preparation and data-acquisition protocols. Further enhancement in data analysis pipelines including more advanced spectral preprocessing, feature selection, and supervised methods of multivariate classification analysis also contribute to taxonomic resolution enhancements. Strains of Staphylococcus aureus. Enterococcus faecium, and Bacillus cereus are selected to illustrate aspects of that strategy. 

The main purposes of multivariate analysis are data reduction (unsupervised analysis) and data modeling like regression and/or classification models (supervised analysis). 

Most of the supervised pattern recognition procedures permit the carrying out of stepwise selection, i.e. the selection first of the most important feature, then, of the second most important, etc. One way to do this is by prediction using e.g. cross-validation (see next section), i.e. we first select the variable that best classifies objects of known classification but that are not part of the training set, then the variable that most improves the classification already obtained with the first selected variable, etc. The results for the linear discriminant analysis of the EU/HYPER classification of Section 33.2.1 is that with all 5 or 4 variables a selectivity of 91.4% is obtained and for 3 or 2 variables 88.6% [2] as a measure of classification success. Selectivity is used here. It is applied in the sense of Chapter... 

Remote sensing techniques have been successfully applied for the identification of rocks in Cape Smith fold belt region. Principal Component Analysis is very effective for the separation of gabbro, metabasalt and peridotite. Band Ratio was helpful for the preliminary identification of peridotite. Supervised Classification approach is taken to verify the results obtained by Principal Component Analysis and Band Ratio. It is also useful to remap the unknown regions once the results are verified. 

Exploratory data analysis shows the aptitude of an ensemble of chemical sensors to be utilized for a given application, leaving to the supervised classification the task of building a model to be used to predict the class membership of unknown samples. 

Exploration analysis is not adequate when the task of the analysis is clearly defined. An example is the attribution of each measurement to a pre-defined set of classes. In these cases it is necessary to find a sort of regression able to assign each measurement to a class according to some pre-defined criteria of class membership selection. This kind of analysis is called supervised classification. The information about which classes are present have to be acquired from other considerations about the application under study. Once classes are defined, supervised classification may be described as the search for a model of the following kind ... 

Pattern recognition can be classified according to several parameters. Below we discuss only the supervised/unsupervised dichotomy because it represents two different ways of analyzing hyperspectral data cubes. Unsupervised methods (cluster analysis) classify image pixels without calibration and with spectra only, in contrast to supervised classifications. Feature extraction methods [21] such as PCA or wavelet compression are often applied before cluster analysis. 

Classification, or the division of data into groups, methods can be broadly of two types supervised and unsupervised. The primary difference is that prior information about classes into which the data fall is known and representative samples from these classes are available for supervised methods. The supervised and unsupervised approaches loosely lend themselves into problems that have prior hypotheses and those in which discovery of the classes of data may be needed, respectively. The division is purely for organization purposes in many applications, a combination of both methods can be very powerful. In general, biomedical data analysis will require multiple spectral features and will have stochastic variations. Hence, the field of statistical pattern recognition [88] is of primary importance and we use the term recognition with our learning and classification method descriptions below. 

Fig. 8.11. The classifier development process. Clinical knowledge provides us with a set of classes for supervised classification (top, right). Large numbers of spectra from large sample numbers are reduced to a set of potentially useful features (top, left) or metrics. A modified Bayesian algorithm operates on the metrics to provide predictions that are compared to a gold standard. The end result of the training and validation process is an optimized algorithm, metric set, calibration and validation statistics, and sensitivity analysis of the data...

Supervised learning methods - multivariate analysis of variance and discriminant analysis (MVDA) - k nearest neighbors (kNN) - linear learning machine (LLM) - BAYES classification - soft independent modeling of class analogy (SIMCA) - UNEQ classification Quantitative demarcation of a priori classes, relationships between class properties and variables... 

Questions of type (2.1) may be answered by analysis of variance or by discriminant analysis. All these methods may be found under the name supervised learning or supervised pattern recognition methods. In the sense of question (2.1.3) one may speak of supervised classification or even better of re-classification methods. In situations of type (2.2) methods from the large family of regression methods are appropriate. 

A key clinical classification of breast cancer tumors is estrogen receptor (ER) expression. The more ERs are present on tumor cells, the more likely an anti-estrogen therapy such as tamoxifen can be successfully applied. Classification of each tumor is important, as only about 60%) of breast cancers are ER-positive. Initial studies set out to demonstrate that breast cancers with distinct pathological features could be separated by microarrays. Several groups demonstrated that supervised data analysis could be used to distinguish ER-positive from ER-negative tumors (74—76). 

The goal of classification, also known as discriminant analysis or supervised learning, is to obtain rules that describe the separation between known groups of observations. Moreover, it allows the classification of new observations into one of the groups. We denote the number of groups by Z and assume that we can describe our experiment in each population icj by a / -dimensional random variable Xj with distribution function (density) fj. We write pj for the membership probability, i.e., the probability for an observation to come from icj. 

This supervised classification method, which is the most used, accepts a normal multivariate distribution for the variables in each population ((Ai,..., A ) Xi) ), and calculates the classification functions minimising the possibility of incorrect classification of the observations of the training group (Bayesian type rule). If multivariate normality is accepted and equality of the k covariance matrices ((Ai,..., Xp) NCfti, X)), Linear Discriminant Analysis (LDA) calculates... 

St/pen/7sed Data Mining. Searching large volumes of data for hidden predictive relationships. Supervised analysis requires one or more "dependent" or response variables, to be predicted from a set of "independent" or predictor variables. The techniques used include various classification methods (decision tree, support vector, Bayesian) and various estimation methods (regression, neural nets). 

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