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The classification process of the Counter Propagation neural network (CPN) is investigated. The homogeneity distribution of the codebook vectors is a key element in the accuracy of the classification process. The paper defines an appropriate homogeneity measure that is strongly correlated with the optimal misclassification error. Based on this homogeneity value, the paper proposes three modification...
In this paper we present a possibilistic classifier of multispectral remotely sensed images. This classifier developed in the framework of possibility theory is based on a fusion process using several kinds of combination operators (conjunctive and disjunctive). Unlike the probabilistic classifier which can model only the data uncertainty through a probability measure, the possibilistic classifier...
Type-1 fuzzy set (T1 FS) theory was first introduced by Zadeh in 1965 and has been successfully applied to many fields. In recent years type-2 fuzzy set (T2 FS) system has been attracting research interests and some good results were reported. Type-2 fuzzy sets as those sets whose membership grades themselves type-1 fuzzy sets. Therefore, a type-2 fuzzy system can model the randomness and fuzziness...
As there are climatic changes, there are many risks involved in predicting these changes and make decisions on some infrastructure due to extreme weather conditions and shifting weather patterns. Existing research indicates that small increase in the climate and weather extremes which have the potential to bring large calamities to the existing infrastructures. These changes may be due to the presence...
In real world applications, data are often uncertain or imperfect. In most classification approaches, they are transformed into precise data. However, this uncertainty is an information in itself which should be part of the learning process. Data uncertainty can take several forms: probabilities, (fuzzy)sets of possible values, expert assessments, etc. We therefore need a flexible and generic enough...
Analyses show that the absorption band position determines the type of mineral radically. The paper proposes a method of applying GA (Genetic Algorithm) to the selection of the uranium mineral band feature sub-set. First, on the fundamental of the correlation between feature-based metrics: information entropy, information gain, symmetrical uncertainty and type space, the GA which is a random search...
In order to focus on the hard classes in a multi-class classification task, a critical class oriented query strategy is proposed, which combines the concepts of “guided learning” and “active learning”. In conjunction with the SVM classifier, hard pair classes are first identified based on the instability of the classification hyperplane, whereby category level guidance for which class should be queried...
Data uncertainty is common in real-world applications. Various reasons lead to data uncertainty, including imprecise measurements, network latency, outdated sources and sampling errors. These kinds of uncertainties have to be handled cautiously, or else the data mining results could be unreliable or wrong. In this demo, we will show uRule, a new rule-based classification and prediction system for...
A new method is presented which combines a deterministic analytical method and a probabilistic measure to classify rock types on the basis of their hyperspectral curve shape. This method is a supervised learning algorithm using Gaussian Processes (GPs) and the Observation Angle Dependent (OAD) covariance function. The OAD covariance function makes use of the properties of the Spectral Angle Mapper...
Traditional decision tree classifiers work with data whose values are known and precise. We extend such classifiers to handle data with uncertain information, which originates from measurement/quantisation errors, data staleness, multiple repeated measurements, etc. The value uncertainty is represented by multiple values forming a probability distribution function (pdf). We discover that the accuracy...
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