Temat: Metoda najmniejszych kwadratów - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Maximum Separation Partial Least Squares (MSPLS): a new method for classification in microarray experiment
Autorzy:: Błaszczyk, P.
Stąpor, K.
Powiązania:: https://bibliotekanauki.pl/articles/333834.pdf
Data publikacji:: 2007
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: klasyfikacja
metoda cząstkowych najmniejszych kwadratów
eksperyment mikromacierzowy
partial least squares
classifications
maximal separation criterion
microarray experiment
supervisor learning
Opis:: The purpose of the paper is to propose a new method for classification. Our MSPLS method was deduced from the classic Partial Least Squares (PLS) algorithm. In this method we applied the Maximum Separation Criterion. On the basis of the approach we are able to find such weight vectors that the dispersion between the classes is maximal and the dispersion within the classes is minimal. In order to compare the performance of classifier we used the following types of dataset - biological and simulated. Error rates and confidence intervals were estimated by the jackknife method.
Źródło:: Journal of Medical Informatics & Technologies; 2007, 11; 187-195
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Stability of gene selection methods for multiclass clssification
Autorzy:: Student, S.
Fujarewicz, K.
Powiązania:: https://bibliotekanauki.pl/articles/333948.pdf
Data publikacji:: 2010
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: selekcja genów
metoda cząstkowych najmniejszych kwadratów
klasyfikacja wieloklasowa
gene selection
partial least squares
stability selection
bootstrap .632+
multiclass classification
BBFR
Opis:: A big problem in applying DNA microarrays for classification is dimension of the dataset. Recently we proposed a gene selection method based on Partial Least Squares (PLS) for searching best genes for classification. The new idea is to use PLS not only as multiclass approach, but to construct more binary selections that use one versus rest and one versus one approaches. Ranked gene lists are highly instable in the sense, that a small change of the data set often leads to big change of the obtained ordered list. In this article, we take a look at the assessment of stability of our approaches. We compare the variability of the obtained ordered lists from proposed methods with well known Recursive Feature Elimination (RFE) method and classical t-test method. This paper focuses on effective identification of informative genes. As a result, a new strategy to find small subset of significant genes is designed. Our results on real cancer data show that our approach has very high accuracy rate for different combinations of classification methods giving in the same time very stable feature rankings.
Źródło:: Journal of Medical Informatics & Technologies; 2010, 15; 101-107
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Using Multiclass SVM methods for classification of DNA microarray data
Autorzy:: Student, S.
Powiązania:: https://bibliotekanauki.pl/articles/333907.pdf
Data publikacji:: 2007
Wydawca:: Uniwersytet Śląski. Wydział Informatyki i Nauki o Materiałach. Instytut Informatyki. Zakład Systemów Komputerowych
Tematy:: metoda cząstkowych najmniejszych kwadratów
maszyna wektorów nośnych
Partial Least Squares PLS
dimension reductions
MMulticlass Support Vector Machines MSVM
One-Versus-One OvO
One-Versus RestOvR
Opis:: One important application of gene expression microarray data is classification of samples into categories, such as the type of tumor. A classifier using Multiclass SVM [4] (Support Vector Machines) is described in this article. Our classifier involves dimension reduction using Multivariate Partial Least Squares (MPLS) for classification more than two classes. We use also two methods based on binary classifications: One-Against-All [5] and One-Against-One [6]. These three methods have been tested on a data set involving 125 tumor/normal thyroid human DNA microarrays samples. There are 66 Papillary throid carcinoma, 32 follicular throid carcinoma and 27 normal tissues. The most important thing is to find small number of genes that discriminate between these three classes with good accuracy. The best genes can be selected for Q-PCR validation. Molecular markers differentiating between throid cancer and normal tissues can help in clinical diagnostics and therapy methods. For error estimation we are use the bootstrap .632 [8] technique. Major issue with bootstrap estimators is their high computational cost. That is why we use a OpenMosix with MPI (Message Passing Interface) cluster technology for this system for parallel computation space.
Źródło:: Journal of Medical Informatics & Technologies; 2007, 11; 197-204
1642-6037
Pojawia się w:: Journal of Medical Informatics & Technologies
Dostawca treści:: Biblioteka Nauki

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