An automated approach to the design of decision tree classifiers

The classification of large dimensional data sets arising from the merging of remote sensing data with more traditional forms of ancillary data is considered. Decision tree classification, a popular approach to the problem, is characterized by the property that samples are subjected to a sequence of decision rules before they are assigned to a unique class. An automated technique for effective decision tree design which relies only on apriori statistics is presented. This procedure utilizes a set of two dimensional canonical transforms and Bayes table look-up decision rules. An optimal design at each node is derived based on the associated decision table. A procedure for computing the global probability of correct classfication is also provided. An example is given in which class statistics obtained from an actual LANDSAT scene are used as input to the program. The resulting decision tree design has an associated probability of correct classification of .76 compared to the theoretically optimum .79 probability of correct classification associated with a full dimensional Bayes classifier. Recommendations for future research are included

An ad hoc map evaluation procedure

An ad hoc map evaluation procedure is proposed which is most suitable for evaluating low-resolution classification maps against high resolution ground truth maps, such as maps against interpreted aircraft photographs. Commonly practiced sampling and evaluation procedures are impracticable in this context because of difficulties in registration and in comparing the samples. This ad hoc procedure is designed to overcome these two major problems, and its practicability is discussed. Two widely accepted parameters are estimated by the new procedure; namely, the probability of correct classification and the proportion biases. Statistical qualifications are also provided

A parametric multiclass Bayes error estimator for the multispectral scanner spatial model performance evaluation

The author has identified the following significant results. The probability of correct classification of various populations in data was defined as the primary performance index. The multispectral data being of multiclass nature as well, required a Bayes error estimation procedure that was dependent on a set of class statistics alone. The classification error was expressed in terms of an N dimensional integral, where N was the dimensionality of the feature space. The multispectral scanner spatial model was represented by a linear shift, invariant multiple, port system where the N spectral bands comprised the input processes. The scanner characteristic function, the relationship governing the transformation of the input spatial, and hence, spectral correlation matrices through the systems, was developed

Using the Linear Discriminant Analysis Method to Classify Types of Bowels and Esophageal cancer in Jordan

The research aims at achieving the best linear model to distinguish between two types of Bowels and Esophageal cancer in Jordan, using the methodofdiscriminant analysis, the SPSS program was used to analyze the data. The study concluded a number of results, the most prominent of which were: the variables sex (x1), weight (x3), and Platelets Count P.C (x8) which have a significant impact in constructing the discriminatory function. The probability of correct classification of a disease belonging to the first group was equal to (62.8%) and to the second group was equal to (77%). The probability of misclassification in the first group, was equal to (37.2%), and for the second group was (23%), the overall correct classification ratio (71.6%) and the false classification ratio (28.4%),the probability of correct classification of a disease belonging to the first group was equal to (66.4%) and the second group was equal to (77.6%). It was noted that the discriminant analysis method was able to identify the most important independent variables in the diagnosis of both types of Bowel and Esophageal cancer

Estimating the Conditional Probability of Correct Classification Using Dependent Training Samples

1 online resource (PDF, 27 pages

Comparison between Conventional and Adjusted Mean Probability of Correct Classification for Two Groups Problem: A Preliminary Study

This paper describes a new approach to determine classification performance based on the computation and application of margin of error. This procedure revealed that as the proportion of contamination increases, the misclassification rate and the margin of error also increases. On the other hand, if the mean probability of correct classification is approaching the mean of the optimal probability, the margin of error tends to reduce maximally. The upper and lower classification limits enable us to determine the performance of the technique of interest. If the computed mean probability exceed the upper classification limit this indicates that the rate of misclassification is high. In a general note, we are   confident of the classification result based on this approach. This new technique was applied to investigate the performance of the Fisher linear classification analysis, Fisher’s approach based on the minimum covariance determinant and the probability based classification technique. In general, the performance analysis revealed that as the proportion of contamination increases, the misclassification rate increases thereby producing large margin of error. The implication of large margin of error to classification rule is that the adjusted mean probability based on the margin of error will overshot the upper classification limit which indicates high misclassification rate or possibly highly contaminated data set. Keywords: Classification, Robust, Mean probability, Margin of error 2010 Mathematics Subject Classification:62H99,62M2

Analytical design of multispectral sensors

An optimal design based on the criterion of minimum mean square representation error using the Karhunen-Loeve expansion was developed to represent the spectral response functions from a stratum based upon a stochastic process scene model. From the overall pattern recognition system perspective, the effect of the representation accuracy on a typical performance criterion (the probability of correct classification) is investigated. The optimum sensor design provides a standard against which practical (suboptimum) operational sensors can be compared. An example design is provided and its performance is illustrated. Although developed primarily for the purpose of sensor design, the procedure has potential for making important contributions to scene understanding. Spectral channels which have narrow bandwidths relative to current sensor systems may be necessary to provide adequate spectral representation and improved classification performance