Browsing by Subject "decision tree"

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Access status: Open Access ,
Comparison of machine-learning algorithms for SPOT 7 multispectral image classification
(Wydawnictwa AGH, 2025) Morale, Davide; Parente, Claudio; Bolognesi, Salvatore Falanga
Precise and timely land-cover identification plays an important role in effective environmental monitoring and land management. This study compares the performance of five machine-learning classifiers – support vector machine (SVM), decision tree (DT), normal Bayes (NB), random forest (RF), and k-nearest neighbor (k-NN) – in the land-cover mapping of the Agro Nocerino Sarnese area (Southern Italy) using high-resolution SPOT 7 pan-sharpened multispectral images with a pixel size of 1.5 m × 1.5 m. The data set consisted of blue, green, red, and near-infrared (NIR) bands and was processed with Orfeo ToolBox (OTB) software. Two data sets were analyzed: DS-3B (which included only the visible bands [blue, green, and red]), and DS-4B (which also included the NIR band). A comparison of the classifiers’ performances across various land-cover classes was conducted in order to assess their respective classification accuracy. The results showed that SVM and k-NN achieved the highest overall accuracy levels (93% and 92%, respectively) using only the visible bands, whereas the decision tree classifier performed best when the NIR band was included. Random forest achieved excellent accuracy in vegetation classes (88–99%) but struggled with misclassifications in bare soil and man-made classes such as buildings and roads. These results emphasized the significant impact of data set characteristics on classifier performance as well as the importance of band selection and pan-sharpening techniques in high-resolution land-cover mapping.
Access status: Open Access ,
Metoda automatycznego wyznaczania indeksu mitotycznego populacji komórek cebuli z wykorzystaniem drzewa decyzyjnego
(Wydawnictwa AGH, 2008) Gocławski, Jarosław; Sekulska-Nalewajko, Joanna; Anioł, Patryk
The evaluation of mitotic index is the method of estimation of cell division ability in cell populations treated by growth inhibitors or accelerators. The image processing algorithms for the segmentation of onion cells and their nuclei elements appearing in the process of mitosis is proposed. Then a set of geometrical, textural and topological features of nuclei elements was extracted, which can distinguish interphase from the stages of mitosis. A decision tree was built according to C4.5 method using the maximum of information gain ratio of the feature values. To evaluate classification error, a series of 10-fold crossvalidations were performed. The feature space was reduced by applying PCA method. The value of mitotic index for the tested onion cell population as well as the estimator index error was evaluated. The errors were compared with an average classification error.
Access status: Open Access ,
Nadzorowana kategoryzacja tekstów angielskojęzycznych
(Wydawnictwa AGH, 2010) Chmiel, Wojciech; Kadłuczka, Piotr; Jędrusik, Stanisław
Text classification is a growing area of research at the intersection of information retrieval (IR) and machine learning. The goal of text classification systems is to attach automatically labels to previously unseen electronic documents. These labels may indicate topics discussed in the document, the relevance of the document for a given user, the mailbox or newsgroup into which the document should be filed. Text categorization presents unique challenges due to the large number of attributes present in the data set, large number of training samples, and attribute dependencies. In this paper we present a supervised classification algorithm based on centroids method and decision trees. This paper presents comprehensive computational experiments examining the efficiency of proposed classification algorithms.
Access status: Open Access ,
Rule modeling of ADI cast iron structure for contradictory data
(Wydawnictwa AGH, 2022) Soroczyński, Artur; Biernacki, Robert; Kochański, Andrzej Witold
Ductile iron is a material that is very sensitive to the conditions of crystallization. Due to this fact, the data on the cast iron properties obtained in tests are significantly different and thus sets containing data from samples are contradictory, i.e. they contain inconsistent observations in which, for the same set of input data, the output values are significantly different. The aim of this work is to try to determine the possibility of building rule models in conditions of significant data uncertainty. The paper attempts to determine the impact of the presence of contradictory data in a data set on the results of process modeling with the use of rule-based methods. The study used the well-known dataset (Materials Algorithms Project Data Library, n.d.) pertaining to retained austenite volume fraction in austempered ductile cast iron. Two methods of rulebased modeling were used to model the volume of the retained austenite: the decision trees algorithm (DT) and the rough sets algorithm (RST). The paper demonstrates that the number of inconsistent observations depends on the adopted data discretization criteria. The influence of contradictory data on the generation of rules in both algorithms is considered, and the problems that can be generated by contradictory data used in rule modeling are indicated.