Supervised Classification

Classification (supervised learning) of items or objects in classes according to a given probability... 

Wu, C. H., Chen, H. L. Chen, S. C. (1997). Counter-propagation neural networks for molecular sequence classification Supervised LVQ and dynamic node allocatioa Applied Intelligence 7, 27-38. 

CLASSIFICATION SUPERVISED LEARNING WITH HIGH-DIMENSIONAL BIOLOGICAL DATA... 

C. Wu, H.-L. Chen, and S.-C. Chen, Appl. Intell., 7,27 (1997). Counter-Propagation Neural Networks for Molecular Sequence Classification Supervised LVQ and Dynamic Node Allocation. 

Classification describes the process of assigning an instance or property to one of several given classes. The classes are defined beforehand and this class assignment is used in the learning process, which is therefore supervised. Statistical methods and decision trees (cf. Section 9.3) are also widely used for classification tasks. 

Supervised Learning. Supervised learning refers to a collection of techniques ia which a priori knowledge about the category membership of a set of samples is used to develop a classification rule. The purpose of the rule is usually to predict the category membership for new samples. Sometimes the objective is simply to test the classification hypothesis by evaluating the performance of the rule on the data set. 

If Article 505 is used, area classification, wiring, and equipment selection shall be under the supervision of a qualified Registered Professional Engineer. 

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... 

D. Coomans and D.L. Massart, Alternative K-nearest neighbour rules in supervised pattern recognition. Part 2. Probabilistic classification on the basis of the kNN method modified for direct density estimation. Anal. Chim. Acta, 138 (1982) 153-165. 

ANNs need supervised learning schemes and can so be applied for both classification and calibration. Because ANNs are nonlinear and model-free approaches, they are of special interest in calibration. 

The basis of classification is supervised learning where a set of known objects that belong unambiguously to certain classes are analyzed. From their features (analytical data) classification rules are obtained by means of relevant properties of the data like dispersion and correlation. 

Fig. 6. Supervised Classification image with peridotite, gabbro, metabasalt, metasediments, are shown as Red, Green, Blue, and Yellow.

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 ... 

This section will focus on classification methods, or supervised learning methods, where a method is developed using a set of calibration samples and complete prior knowledge about the class membership of the samples. The development of any supervised learning method involves three steps ... 

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