Temat: decision tree - Prolib Integro

Skocz do pozycji: 1.

Tytuł:: Analysis of complex decision problems based on cumulative prospect theory
Autorzy:: Dudzińska-Baryła, R.
Tematy:: cumulative prospect theory
complex decision problem
decision tree; Pokaż więcej
Wydawca:: Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
Powiązania:: https://bibliotekanauki.pl/articles/406332.pdf Link otwiera się w nowym oknie
Opis:: Complex risky decision problems involve sequences of decisions and random events. The choice at a given stage depends on the decisions taken in the previous stages, as well as on the realizations of the random events that occurred earlier. In the analysis of such situations, decision trees are used, and the criterion for choosing the optimal decision is to maximize the expected monetary value. Unfortunately, this approach often does not reflect the actual choices of individual decision makers. In descriptive decision theory, the criterion of maximizing the expected monetary value is replaced by a subjective valuation that takes into account the relative outcomes and their probabilities. This paper presents a proposal to use the principles of cumulative prospect theory to analyse complex decision problems. The concept of a certainty equivalent is used to make it possible to compare risky and non-risky alternatives.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 2.

Tytuł:: Friedman and Wilcoxon Evaluations Comparing SVM, Bagging, Boosting, K-NN and Decision Tree Classifiers
Autorzy:: Biju, V. G.
Prashanth, CM
Tematy:: bagging
boosting
SVM
KNN
decision tree; Pokaż więcej
Wydawca:: Społeczna Akademia Nauk w Łodzi
Powiązania:: https://bibliotekanauki.pl/articles/108646.pdf Link otwiera się w nowym oknie
Opis:: This paper describes a number of experiments to compare and validate the performance of machine learning classifiers. Creating machine learning models for data with wide varieties has huge applications in predictive modelling across multiple domain of science. This work reviews state of the art techniques in machine learning classifiers methods with several extent of magnitude in statistics and key findings that will be helpful in establishing best methodological practices for class predictions. Comprehensive comparative review analysis with statistical validations for various machine learning algorithm for SVM, Bagging, Boosting, Decision Trees and Nearest Neighborhood algorithm on multiple data sets is carried out. Focus on the statistical analysis of the results using Friedman-Test and Wilcoxon Test as well as other interpretative metrics like classification rate, ROC, F-measure are evaluated to benchmark results.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 3.

Tytuł:: Features of Creating a System of Space Monitoring of Water-Supplied Territories for Irrigation in the South of Kazakhstan
Autorzy:: Malakhov, Dmitry V.
Tskhay, Mikhail
Kalashnikov, Alexander A.
Bekmukhamedov, Nurlan E.
Kalashnikov, Pavel A.
Baizakova, Aigul
Tematy:: irrigation
spectral indices
decision tree
monitoring
evaluation; Pokaż więcej
Wydawca:: Polskie Towarzystwo Inżynierii Ekologicznej
Powiązania:: https://bibliotekanauki.pl/articles/2202277.pdf Link otwiera się w nowym oknie
Opis:: The location of a significant part of the agricultural territories of Kazakhstan in the risk agriculture zone implies the development and further application of an objective monitoring system for irrigated territories. The purpose of the study was to develop methods for on-the-spot and long-term recognition of irrigated massifs and verification of methods in the conditions of the territories of southern Kazakhstan. The paper describes the methods of on-the-spot recognition of irrigated fields, the general assessment of irrigated areas for the growing season, as well as the method of recognizing promising areas for irrigation. The on-the-spot recognition of the fields is based on the use of such spectral indices as the Global Vegetation Moisture Index, Green Normalized Difference Vegetation Index, Normalized Difference Vegetation Index, and the xanthophyll index, combined into a single system by the Decision Tree algorithm. The assessment of irrigated areas is based on differences in the physiological state of plants in conditions of normal water supply and plants experiencing a lack of moisture. The evaluation system includes the calculation of the temperature difference according to the corresponding satellite data and the calculation of the difference in vegetation indices for the same period. The difference in vegetation indices in irrigated fields has positive values due to a steady increase in green biomass, and the temperature difference, on the contrary, is negative or zero, since healthy plants, with normal water supply, actively evaporate moisture to maintain optimal temperatures of biochemical processes. To develop these methods, ground data from 2017–2021 were used. Verification of the methods with ground data demonstrated acceptable accuracy (87% in the on-the-spot assessment of irrigated fields; 60–90% in the general assessment of irrigated areas), while the methods have significant potential for further improvement.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 4.

Tytuł:: Hardware implementation of a decision tree classifier for object recognition applications
Autorzy:: Fularz, M.
Kraft, M.
Tematy:: decision tree
hardware implementation
FPGA
object recognition; Pokaż więcej
Wydawca:: Stowarzyszenie Inżynierów i Techników Mechaników Polskich
Powiązania:: https://bibliotekanauki.pl/articles/114595.pdf Link otwiera się w nowym oknie
Opis:: Hardware implementation of a widely used decision tree classifier is presented in this paper. The classifier task is to perform image-based object classification. The performance evaluation of the implemented architecture in terms of resource utilization and processing speed are reported. The presented architecture is compact, flexible and highly scalable and compares favorably to software-only solutions in terms of processing speed and power consumption.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 5.

Tytuł:: Methods of Classiﬁcation of the Genera and Species of Bacteria Using Decision Tree
Autorzy:: Plichta, Anna
Tematy:: bacterial genera and species
decision tree
pattern recognition; Pokaż więcej
Wydawca:: Instytut Łączności - Państwowy Instytut Badawczy
Powiązania:: https://bibliotekanauki.pl/articles/308707.pdf Link otwiera się w nowym oknie
Opis:: This paper presents a computer-based method for recognizing digital images of bacterial cells. It covers automatic recognition of twenty genera and species of bacteria chosen by the author whose original contribution to the work consisted in the decision to conduct the process of recognizing bacteria using the simultaneous analysis of the following physical features of bacterial cells: color, size, shape, number of clusters, cluster shape, as well as density and distribution of the cells. The proposed method may be also used to recognize the microorganisms other than bacteria. In addition, it does not require the use of any specialized equipment. The lack of demand for high infrastructural standards and complementarity with the hardware and software widens the scope of the method’s application in diagnostics, including microbiological diagnostics. The proposed method may be used to identify new genera and species of bacteria, but also other microorganisms that exhibit similar morphological characteristics.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 6.

Tytuł:: Automated root cause analysis of non-conformities with machine learning algorithms
Autorzy:: Mueller, T.
Greipel, J.
Weber, T.
Schmitt, R. H.
Tematy:: root cause analysis
machine learning
decision tree
simulation; Pokaż więcej
Wydawca:: Wrocławska Rada Federacji Stowarzyszeń Naukowo-Technicznych
Powiązania:: https://bibliotekanauki.pl/articles/99625.pdf Link otwiera się w nowym oknie
Opis:: To detect root causes of non-conforming parts - parts outside the tolerance limits - in production processes a high level of expert knowledge is necessary. This results in high costs and a low flexibility in the choice of personnel to perform analyses. In modern production a vast amount of process data is available and machine learning algorithms exist which model processes empirically. Aim of this paper is to introduce a procedure for an automated root cause analysis based on machine learning algorithms to reduce the costs and the necessary expert knowledge. Therefore, a decision tree algorithm is chosen. A procedure for its application in an automated root cause analysis is presented and simulations to prove its applicability are conducted. In this paper influences affecting the success of detection are identified and simulated e.g. the necessary amount of data dependent on the amount of variables, the ratio between categories of non-conformities and OK parts as well as detectable root causes. The simulations are based on a regression model to determine the roughness of drilling holes. They prove the applicability of machine learning algorithms for an automated root cause analysis and indicate which influences have to be considered in real scenarios.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 7.

Tytuł:: Optimising a fuzzy fault classification tree by a single-objective genetic algorithm
Autorzy:: Zio, E.
Baraldi, P.
Popescu, I. C.
Tematy:: fault classification
decision tree
fuzzy logic
genetic algorithm; Pokaż więcej
Wydawca:: Uniwersytet Morski w Gdyni. Polskie Towarzystwo Bezpieczeństwa i Niezawodności
Powiązania:: https://bibliotekanauki.pl/articles/2069595.pdf Link otwiera się w nowym oknie
Opis:: In this paper a single-objective Genetic Algorithm is exploited to optimise a Fuzzy Decision Tree for fault classification. The optimisation procedure is presented with respect to an ancillary classification problem built with artificial data. Work is in progress for the application of the proposed approach to a real fault classification problem.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 8.

Tytuł:: Application of sample advisory systems in medicine
Autorzy:: Woehl, Agnieszka
Zapotoczny, Kacper
Żaba, Julia
Nagi, Łukasz
Tematy:: advisory system
expert system
block diagram
decision tree; Pokaż więcej
Wydawca:: Politechnika Opolska
Powiązania:: https://bibliotekanauki.pl/articles/34656174.pdf Link otwiera się w nowym oknie
Opis:: Artificial intelligence is a field that has been rapidly developing in various areas of knowledge in recent years. Its application in medicine can support the intensive development of research in health care and improve and ac-celerate the operation of many medical facilities. This article presents sev-eral examples of expert systems that can find application in diagnosing and preparing a patient for selected tests. Expert systems can also find appli-cation in the rapid selection of rehabilitation, medical or support equip-ment and devices with which medical facilities are supplied. In this article, the reader will also find a sample application that will perform this func-tion. The article presents the elements of which a correct expert system should consist. For each application, tests have been carried out to show the correctness of the system. The purpose of the article was to show the capabilities of the expert system and its application in medical fields.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 9.

Tytuł:: Using reinforcement learning to select an optimal feature set
Autorzy:: Akhiat, Yassine
Zinedine, Ahmed
Chahhou, Mohamed
Tematy:: feature selection
data mining
decision tree
reinforcement learning
dimensionality reduction; Pokaż więcej
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Powiązania:: https://bibliotekanauki.pl/articles/59115467.pdf Link otwiera się w nowym oknie
Opis:: Feature Selection (FS) is an essential research topic in the area of machine learning. FS, which is the process of identifying the relevant features and removing the irrelevant and redundant ones, is meant to deal with the high dimensionality problem for the sake of selecting the best performing feature subset. In the literature, many feature selection techniques approach the task as a research problem, where each state in the search space is a possible feature subset. In this paper, we introduce a new feature selection method based on reinforcement learning. First, decision tree branches are used to traverse the search space. Second, a transition similarity measure is proposed so as to ensure exploit-explore trade-off. Finally, the informative features are the most involved ones in constructing the best branches. The performance of the proposed approaches is evaluated on nine standard benchmark datasets. The results using the AUC score show the effectiveness of the proposed system.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Skocz do pozycji: 10.

Tytuł:: Computational Intelligence for Analysing the Mechanical Properties of AA 2219 - (B4C + h-BN) Hybrid Nano Composites Processed by Ultrasound Assisted Casting
Autorzy:: Radha, P.
Selvakumar, N.
Harichandran, R.
Tematy:: powder metallurgy
soft computing
ANN
fuzzy logic
decision tree; Pokaż więcej
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Powiązania:: https://bibliotekanauki.pl/articles/354927.pdf Link otwiera się w nowym oknie
Opis:: The computational intelligence tool has major contribution to analyse the properties of materials without much experimentation. The B₄ C particles are used to improve the quality of the strength of materials. With respect to the percentage of these particles used in the micro and nano, composites may fix the mechanical properties. The different combinations of input parameters determine the characteristics of raw materials. The load, content of B₄ C particles with 0%, 2%, 4%, 6%, 8% and 10% will determine the wear behaviour like CoF, wear rate etc. The properties of materials like stress, strain, % of elongation and impact energy are studied. The temperature based CoF and wear rate is analysed. The temperature may vary between 30°C, 100°C and 200°C. In addition, the CoF and wear rate of materials are predicted with respect to load, weight % of B₄ C and nano hexagonal boron nitride %. The intelligent tools like Neural Networks (BPNN, RBNN, FL and Decision tree) are applied to analyse these characteristics of micro/nano composites with the inclusion of B₄ C particles and nano hBN % without physically conducting the experiments in the Lab. The material properties will be classified with respect to the range of input parameters using the computational model.
Dostawca treści:: Biblioteka Nauki

Artykuł

na półce

Informacja

Wyszukujesz frazę "decision tree" wg kryterium: Temat