Algorithms and techniques for efficient and effective nearest neighbours classification


Although the k-NN classifier is considered to be an effective classification algorithm, it has some major weaknesses that may render its use inappropriate for some application domains and / or datasets. The first one is the high computational cost involved (all distances between each unclassified item and all training data must be computed). Although nowadays systems are equipped with powerful processors, in cases of large datasets, this drawback renders the classification a time-consuming and in some cases a prohibitive procedure. Another weakness is the high storage requirements for maintaining the training data. Eager classifiers (e.g., decision tress, neural networks) can discard the training data after the construction of the classification model in order to save space. In contrast, the k-NN classifier must have all the training data always available. Moreover, the classification accuracy achieved by the classifier depends on the quality of the available training data. Noisy and ...
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Alternative title
Αλγόριθμοι και τεχνικές για αποδοτική και αποτελεσματική κατηγοριοποίηση εγγυτέρων γειτόνων
Ougiaroglou, Stefanos
Committee members
Ευαγγελίδης Γεώργιος
Δέρβος Δημήτριος
Aldama Montes Jose Francisco
Μαργαρίτης Κωνσταντίνος
Σαμαράς Νικόλαος
Κολωνιάρη Γεωργία
Παπαδόπουλος Απόστολος
Natural Sciences
Computer and Information Sciences
Nearest neighbours; Classification; Clustering; Data reduction / Condensing; Prototype selection and abstraction; Data streams / Dynamic environments; Time-series; Editing (noise removal)
247 σ., tbls., fig., ch.
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