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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Požymių erdvės mažinimo metodų kokybės tyrimas / Comparison of methods for features space reduction

Vaišnoraitė, Giedrė 16 August 2007 (has links)
Magistro darbo tikslas yra tarpusavyje palyginti klasifikavimui skirtų požymių mažinimo metodus, kurie turimą požymių aibę transformuoja į mažesnės eilės aibę. Duomenų klasifikavimo kokybė transformuotoje požymių erdvėje turi nenukentėti. Eksperimentams naudotos keturios realių duomenų bazės. Kiekvienai duomenų bazei tikrinama hipotezė apie vidutinių reikšmių lygybę, t.y. lyginamos dvi skirtingos vidutinės klasifikavimo klaidos ir nuspręsta ar jos yra panašios, ar skirtingos, naudojant Stjudento (t) testą. Tam, kad tai patikrinti bus skaičiuojama T statistika. Pirmą kartą duomenų požymių atrinkimui panaudotas neraiškaus integralo metodas su pilnuoju matu. Visi gauti eksperimentų rezultatai pateikti paveiksluose ir apibendrinti lentelėse. Magistrinio darbo išvadose pateiktas trumpas gautų rezultatų aprašymas. / The process of finding features that meet the given constrains out of a large group of features is called feature reduction. The reduction concept can be divided into feature selection and feature extraction techniques. The feature selection approach selects the independent features that provide sufficient information for a satisfactory separation between the different situations we want to discriminate. The physical values of selected features remain unchanged. The redundancy of features might be identified by a feature clustering and selection algorithm or we might remove features with the highest correlation. The algorithm removes similar features. This implies a faster training of consequent classifiers on reduced feature space. The feature extraction method works in opposite. Hereby, the features are projected onto a set of reduced feature space by some transformation function. The features in transformed space are no longer representing the same physical meaning as in original space. The transformation function is an analytical function and the challenge is to find representative and informative transformation for the given feature set. Very well known techniques are: the principal components analysis (PCA) and dimensionality reduction by auto-associative mapping using MLP neural. Four methods for features space reduction were analyzed in this work. All these methods have been used with four publicly available databases and applied to very well known k-nearest neighbor... [to full text]

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