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Search results for: DECISION TREE
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Construction of a picewise-linear classifier by applaing discriminant analysis to decision tree induction
PublicationArtykuł prezentuje metodę konstrukcji drzew decyzyjnych. W odróżnieniu od większości popularnych algorytmów, które wybierają pojedyncze cechy do budowy reguł decyzyjnych w węzłach drzewa, ta metoda łączy wszystkie cechy. Używa ona wieloklasowego kryterium Fishera do wydzielenia nowych cech, które są liniowa kombinacją cech pierwotnych. Takie drzewa mogą aproksymować złożone regiony decyzyjne używając mniejszej liczby węzłów w porównaniu...
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Multidimensional Feature Selection and Interaction Mining with Decision Tree Based Ensemble Methods
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Adaptive neuro fuzzy and fuzzy decision tree classifiers as applied to sea floor characterization.
PublicationPrzedstawiono wyniki badań wpływu różnych parametrów echa odbitego od dna morskiego na dokładność klasyfikacji typu dna przy pomocy sieci neuronowej z logiką rozmytą i przy pomocy drzew decyzyjnych. W szczególności uwzględniono takie parametry echa jak: energia, amplituda i nachylenie opadającego zbocza, wzbogacone o współczynniki falkowe otrzymane z dyskretnej transformacji falkowej (DWT).
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Expression-based decision tree model reveals distinct microRNA expression pattern in pediatric neuronal and mixed neuronal-glial tumors
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The Risk Indicators of Construction Projects’ Cost Overruns Assessed with PCA, Decision Trees, and Pearson’s Correlations
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Assessing the damage importance rank in acoustic diagnostics of technical conditions of the internal combustion engine with multi-valued logical decision trees
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The Use of Artificial Neural Networks and Decision Trees to Predict the Degree of Odor Nuisance of Post-Digestion Sludge in the Sewage Treatment Plant Process
PublicationThis paper presents the application of artificial neural networks and decision trees for the prediction of odor properties of post-fermentation sludge from a biological-mechanical wastewater treatment plant. The input parameters were concentrations of popular compounds present in the sludge, such as toluene, p-xylene, and p-cresol, and process parameters including the concentration of volatile fatty acids, pH, and alkalinity in...
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Experience-Driven Model of Decision-Making Processes in Project Teams
PublicationThis article presents a model of decision-making processes in project teams. Project teams constitute a specific type of organization appointed to implement a project. Decisions made by project teams result from the methods of project management and best management practices. The authors have undertaken the task of formalizing these processes using the classical method of constructing decision trees. It has been established that...
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A machine learning approach to classifying New York Heart Association (NYHA) heart failure
PublicationAccording to the European Society of Cardiology, globally the number of patients with heart failure nearly doubled from 33.5 million in 1990 to 64.3 million in 2017, and is further projected to increase dramatically in this decade, still remaining a leading cause of morbidity and mortality. One of the most frequently applied heart failure classification systems that physicians use is the New York Heart Association (NYHA) Functional...
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Adding Interpretability to Neural Knowledge DNA
PublicationThis paper proposes a novel approach that adds the interpretability to Neural Knowledge DNA (NK-DNA) via generating a decision tree. The NK-DNA is a promising knowledge representation approach for acquiring, storing, sharing, and reusing knowledge among machines and computing systems. We introduce the decision tree-based generative method for knowledge extraction and representation to make the NK-DNA more explainable. We examine...
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Empirical analysis of tree-based classification models for customer churn prediction
PublicationCustomer churn is a vital and reoccurring problem facing most business industries, particularly the telecommunications industry. Considering the fierce competition among telecommunications firms and the high expenses of attracting and gaining new subscribers, keeping existing loyal subscribers becomes crucial. Early prediction of disgruntled subscribers can assist telecommunications firms in identifying the reasons for churn and...
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Exploring Stock Traders’ Cognitive Biases: Research Design and Simulator Framework
PublicationCognitive bias is a phenomenon that has been extensively studied in stock trading and many other fields. This paper presents a framework for a Mobile Stock Trading Simulator (MSTS) that facilitates automatic investment in stocks with minimal human influence, by investigating the behavioral patterns and cognitive errors of stock market investors. The paper aims to determine whether investors’ investment strategies can be improved...
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The Complexity of Zero-Visibility Cops and Robber
PublicationIn this work we deal with the computational complexity aspects of the zero-visibility Cops and Robber game. We provide an algorithm that computes the zero-visibility copnumber of a tree in linear time and show that the corresponding decision problem is NP-complete even for the class of starlike graphs.
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Narratives on cutting down trees on private land. A comparison of urban and rural municipalities in Poland using the Q-deliberation method
PublicationIncreased development in rural and urban areas leads to a decrease in tree cover and reduces the ecosystem services that trees provide. Municipal authorities must consider managing trees on private land to ensure that residents have access to trees and green spaces. In doing so, they must frequently confront conflicting stakeholder views, which are driven by diverse public and private interests and impacted by the type of landscape...
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The complexity of zero-visibility cops and robber
PublicationWe consider the zero-visibility cops & robber game restricted to trees. We produce a characterisation of trees of copnumber k and We consider the computational complexity of the zero-visibility Cops and Robber game. We present a heavily modified version of an already-existing algorithm that computes the zero-visibility copnumber of a tree in linear time and we show that the corresponding decision problem is NP-complete on a nontrivial...
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Ranking ecosystem services delivered by trees in urban and rural areas
PublicationPolicies and strategies for tree management and protection on a national, regional, and local level have not sufficiently considered differences between rural and urban areas. We used expert knowledge to compare rural and urban areas in a case study evaluating the relative importance of ecosystem services (ES) in policy development. The Analytic Hierarchy Process (AHP) and focus group discussions were used to rank 17 ES, representing...
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Intelligent Decision Forest Models for Customer Churn Prediction
PublicationCustomer churn is a critical issue impacting enterprises and organizations, particularly in the emerging and highly competitive telecommunications industry. It is important to researchers and industry analysts interested in projecting customer behavior to separate churn from non‐churn consumers. The fundamental incentive is a firm’s intent desire to keep current consumers, along with the exorbitant expense of gaining new ones....
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Vehicle classification based on soft computing algorithms
PublicationExperiments and results regarding vehicle type classification are presented. Three classes of vehicles are recognized: sedans, vans and trucks. The system uses a non-calibrated traffic camera, therefore no direct vehicle dimensions are used. Various vehicle descriptors are tested, including those based on vehicle mask only and those based on vehicle images. The latter ones employ Speeded Up Robust Features (SURF) and gradient images...
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Comparative Evaluation of Selected Biological Methods for the Removal of Hydrophilic and Hydrophobic Odorous VOCs from Air
PublicationDue to increasingly stringent legal regulations as well as increasing social awareness, the removal of odorous volatile organic compounds (VOCs) from air is gaining importance. This paper presents the strategy to compare selected biological methods intended for the removal of different air pollutants, especially of odorous character. Biofiltration, biotrickling filtration and bioscrubbing technologies are evaluated in terms of...
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Komputerowo wspomagana klasyfikacja wybranych sygnałów elektromiografii powierzchniowej
PublicationWykorzystywanie sygnałów elektromiografii powierzchniowej (ang. Surface Electromyography, SEMG) w procesach sterowania systemami rehabilitacyjnymi stanowi obecnie standardową procedurę. Popularność SEMG wynika z nieinwazyjności metody oraz możliwości szybkiej i precyzyjnej identyfikacji funkcji mięśniowej. W przypadku osób małoletnich proces klasyfikacji sygnałów jest utrudniony ze względu na mniejsze rozmiary i wyższą dynamikę...
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Domination subdivision and domination multisubdivision numbers of graphs
PublicationThe domination subdivision number sd(G) of a graph G is the minimum number of edges that must be subdivided (where an edge can be subdivided at most once) in order to increase the domination number of G. It has been shown [10] that sd(T)<=3 for any tree T. We prove that the decision problem of the domination subdivision number is NP-complete even for bipartite graphs. For this reason we define the domination multisubdivision number...
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Employing flowgraphs for forward route reconstruction in video surveillance system
PublicationPawlak’s flowgraphs were utilized as a base idea and knowledge container for prediction and decision making algorithms applied to experimental video surveillance system. The system is used for tracking people inside buildings in order to obtain information about their appearance and movement. The fields of view of the cameras did not overlap. Therefore, when an object was moving through unsupervised areas, prediction was needed...
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A comparative analysis of the effectiveness of corporate bankruptcy prediction models based on financial ratios: Evidence from Colombia, 2008 to 2015
PublicationLogit and discriminant analyses have been used for corporate bankruptcy prediction in several studies since the last century. In recent years there have been dozens of studies comparing the several models available, including the ones mentioned above and also probit, artificial neural networks, support vector machines, among others. For the first time for Colombia, this paper presents a comparative analysis of the effectiveness...
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Pawlak's flow graph extensions for video surveillance systems
PublicationThe idea of the Pawlak's flow graphs is applicable to many problems in various fields related to decision algorithms or data mining. The flow graphs can be used also in the video surveillance systems. Especially in distributed multi-camera systems which are problematic to be handled by human operators because of their limited perception. In such systems automated video analysis needs to be implemented. Important part of this analysis...
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News that Moves the Market: DSEX-News Dataset for Forecasting DSE Using BERT
PublicationStock market is a complex and dynamic industry that has always presented challenges for stakeholders and investors due to its unpredictable nature. This unpredictability motivates the need for more accurate prediction models. Traditional prediction models have limitations in handling the dynamic nature of the stock market. Additionally, previous methods have used less relevant data, leading to suboptimal performance. This study...
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Prediction of energy consumption and evaluation of affecting factors in a full-scale WWTP using a machine learning approach
PublicationTreatment of municipal wastewater to meet the stringent effluent quality standards is an energy-intensive process and the main contributor to the costs of wastewater treatment plants (WWTPs). Analysis and prediction of energy consumption (EC) are essential in designing and operating sustainable energy-saving WWTPs. In this study, the effect of wastewater, hydraulic, and climate-based parameters on the daily consumption of EC by...
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Comparative study on total nitrogen prediction in wastewater treatment 1 plant and effect of various feature selection methods on machine learning algorithms performance
PublicationWastewater characteristics prediction in wastewater treatment plants (WWTPs) is valuable and can reduce the number of sampling, energy, and cost. Feature Selection (FS) methods are used in the pre-processing section for enhancing the model performance. This study aims to evaluate the effect of seven different FS methods (filter, wrapper, and embedded methods) on enhancing the prediction accuracy for total nitrogen (TN) in the WWTP...
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Data-driven Models for Predicting Compressive Strength of 3D-printed Fiber-Reinforced Concrete using Interpretable Machine Learning Algorithms
Publication3D printing technology is growing swiftly in the construction sector due to its numerous benefits, such as intricate designs, quicker construction, waste reduction, environmental friendliness, cost savings, and enhanced safety. Nevertheless, optimizing the concrete mix for 3D printing is a challenging task due to the numerous factors involved, requiring extensive experimentation. Therefore, this study used three machine learning...
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Study of various machine learning approaches for Sentinel-2 derived bathymetry
PublicationIn recent years precise and up-to-date information regarding seabed depth has become more and more important for companies and institutions that operate on coastlines. While direct, in-situ measurements are performed regularly, they are expensive, time-consuming and impractical to be performed in short time intervals. At the same time, an ever-increasing amount of satellite imaging data becomes available. With these images, it...
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Ultrasound and Clinical Preoperative Characteristics for Discrimination Between Ovarian Metastatic Colorectal Cancer and Primary Ovarian Cancer: A Case-Control Study
PublicationThe aim of this study was to describe the clinical and sonographic features of ovarian metastases originating from colorectal cancer (mCRC), and to discriminate mCRC from primary ovarian cancer (OC). We conducted a multi-institutional, retrospective study of consecutive patients with ovarian mCRC who had undergone ultrasound examination using the International Ovarian Tumor Analysis (IOTA) terminology, with the addition of evaluating...
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Study of Multi-Class Classification Algorithms’ Performance on Highly Imbalanced Network Intrusion Datasets
PublicationThis paper is devoted to the problem of class imbalance in machine learning, focusing on the intrusion detection of rare classes in computer networks. The problem of class imbalance occurs when one class heavily outnumbers examples from the other classes. In this paper, we are particularly interested in classifiers, as pattern recognition and anomaly detection could be solved as a classification problem. As still a major part of...
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Jacek Namieśnik prof. dr hab. inż.
PeopleScientific discipline: chemistryRector in 2016-2019 He was born on 10 December, 1949 in Mogilno. He graduated in 1972 at the Faculty of Chemistry at Gdańsk University of Technology, obtaining a master's degree in chemical engineering. In 1972 he started working at Gdańsk University of Technology, where in 1978 he defended his doctoral thesis and in 1985 he completed his habilitation. He was appointed an associate professor in 1991...
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Marek Czachor prof. dr hab.
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Influence of accelerometer signal pre-processing and classification method on human activity recognition
PublicationA study of data pre-processing influence on accelerometer-based human activity recognition algorithms is presented. The frequency band used to filter-out the accelerometer signals and the number of accelerometers involved were considered in terms of their influence on the recognition accuracy. In the test four methods of classification were used: support vector machine, decision trees, neural network, k-nearest neighbor.