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Classifying objects

Classify object u with the group to which a majority of the K objects belongs. [Pg.315]

Clustering or cluster analysis is used to classify objects, characterized by the values of a set of variables, into groups. It is therefore an alternative to principal component analysis for describing the structure of a data table. Let us consider an example. [Pg.57]

Mathematically, this means that one needs to assign portions of an 8-dimensionaI space to the three classes. A new sample is then assigned to the class which occupies the portion of space in which the sample is located. Supervised pattern recognition is distinct from unsupervised pattern recognition. In the latter one applies essentially clustering methods (Chapter 30) to classify objects into classes that are not known beforehand. In supervised pattern recognition, one knows the classes and has to decide in which of those an object should be classified. [Pg.207]

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... [Pg.236]

For example, suppose an employee is a person with an employer employees have a salary (see Figure 3.19) and can get fired. This lets us classify objects based on a condition and define resultant properties they will have. For example, we could define two state... [Pg.142]

When using a model with two PLS components for the phenyl data, the thresholds corresponding to 95% are. yi. w 0.22 and yHiGH = 015, respectively. In this way, 36 out of 300 objects from the calibration set are not classified to any of the groups, corresponding to 12.0%. Using the same thresholds for the test set, 37 out of 300 objects (12.3%) are not classified. The evaluation measures for the classified objects are shown in Table 5.5, and they clearly improve compared to Table 5.4. [Pg.258]

Since SIMCA is a class modeling method, class assignment is based on fit of the unknowns to the class models. This assignment allows the classification result that the unknown is none of the described classes, and has the advantage of providing the relative geometric portion of the newly classified object. This makes it possible to assess or quantitate the test sample in terms of external variables that are available for the training sets. [Pg.249]

The statistical classification uses probability models to classify objects. [Pg.214]

The important and classifiable objects of chemical discourse include, but are not... [Pg.155]

You can use comparing and contrasting to help you classify objects or properties, differentiate between similar concepts, and speculate about new relationships. For example, as you read Chapters 1 and 2 you might begin to make a table in which you compare and contrast metals, nonmetals, and metalloids. As you continue to learn about these substances in the chapter on the Periodic Table, you can add to your table, giving you a better understanding of the similarities and differences among elements. [Pg.872]

To be more specific, when classifying objective functions the DM indicates which function values should improve, which ones are acceptable and which are allowed to get worse. In addition, amounts of improvement or impairments are asked from the DM. There exist several classification-based interactive multi-objective optimization methods. They use different numbers of classes and generate new solutions in different ways. [Pg.165]

Discriminant analysis techniques (also called classification techniques) are concerned with classifying objects into one of two or more classes. Discriminant techniques are considered to be learning procedures. Given, a set of objects whose class identity is known, a model learns from the variables which have been measured for each of the objects, a procedure which can be used to assign a new object, whose class identity is unknown, into one of the predefined classes. Such a procedure is performed using a well-defined discriminatory rule. [Pg.437]

The correct classification rate (CCR) or misclassification rate (MCR) are perhaps the most favoured assessment criteria in discriminant analysis. Their widespread popularity is obviously due to their ease in interpretation and implementation. Other assessment criteria are based on probability measures. Unlike correct classification rates which provide a discrete measure of assignment accuracy, probability based criteria provide a more continuous measure and reflect the degree of certainty with which assignments have been made. In this chapter we present results in terms of correct classification rates, for their ease in interpretation, but use a probability based criterion function in the construction of the filter coefficients (see Section 2.3). Whilst we speak of correct classification rates, misclassification rates (MCR == 1 - CCR) would equally suffice. The correct classification rate is typically formulated as the ratio of correctly classified objects with the total... [Pg.440]

Pigeon holes can be used for sorting and classifying objects like mail. [Pg.87]

The results of categorical classification are represented in the confusion matrix, which contains the numbers of correct classified objects in each class on the main diagonal and the misclassified objects in the off-diagonal. [Pg.188]


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