Dariusz Jankowski

Dariusz Jankowski
Wroclaw University of Science and Technology | WUT · Department of Systems and Computer Networks

Ph.D.

About

10
Publications
642
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
69
Citations
Citations since 2016
4 Research Items
66 Citations
201620172018201920202021202205101520
201620172018201920202021202205101520
201620172018201920202021202205101520
201620172018201920202021202205101520
Additional affiliations
October 2016 - present
Wroclaw University of Science and Technology
Position
  • Professor (Assistant)

Publications

Publications (10)
Chapter
In the world where technology has largely dominated almost every aspect of human life the amount of data generated each minute grows at a rapid rate. The need to analyse massive volumes of data poses new challenges for researchers and specialists around the world. The MapReduce model became a center of interest due to offering a way of execution th...
Article
This paper address the data mining task of classifying data stream with concept drift. The proposed algorithm, named Concept-adapting Evolutionary Algorithm For Decision Tree (CEVOT) does not require any knowledge of the environment in which it operates (e.g. numbers and rates of drifts). The novelty of the approach is combining tree learner and ev...
Conference Paper
The paper presents a modified method of building ensembles of tensor classifiers for direct multidimensional pattern recognition in tensor subspaces. The novelty of the proposed solution is a method of lowering tensor subspace dimensions by rotation of the training pattern to their optimal directions. These are obtained computing and analyzing phas...
Chapter
Control of dental millingprocesses is a task which can significantly reduce production costs due to possible savings in time. Appropriate setup of production parameters can be done in a course of optimisation aiming at minimising selected objective function, e.g. time. Nonetheless, the main obstacle here is lack of explicitly defined objective func...
Conference Paper
Clustering is the task of partitioning data objects into groups, so that the objects within a cluster are similar to one another and dissimilar to the objects in other clusters. The efficiency random algorithm for good k is used to estimate the optimal number of clusters. In this research two important clustering algorithms, namely centroid based k...
Article
Full-text available
Nowadays, the hyperspectral imaging is the focus of intense research, because its applications can be very useful in the natural disaster monitoring and agricultural monitoring to enumerate only a few. The main problem of systems using hyperspectral imaging is the cost of labelling, because it requires the domain experts, who label the region or pr...
Chapter
A valid diagnosis of migraine is a non-trivial decision problem. This is due to the fact that migraine can manifest wide range of varied symptoms. Thus, designing a computer aided diagnosis system for that problem remains still a very interesting topic. In this paper we present an ensemble classifier system designed for headache diagnosis. We assum...
Conference Paper
Decision trees are among the most popular classification algorithms due to their knowledge representation in form of decision rules which are easy for interpretation and analysis. Nonetheless, a majority of decision trees training algorithms base on greedy top-down induction strategy which has the tendency to develop too complex tree structures. Th...
Chapter
Headache, medically known as cephalalgia, may have a wide range of symptoms and its types may be related and mixed. Its proper diagnosis is difficult and automatic diagnosis is usually rather imprecise, therefore, the problem is still the focus of intensive research. In the paper we propose headache diagnosis method which makes the decision on the...

Network

Cited By

Projects

Project (1)
Project
The project relates to machine learning algorithms for data stream classification. The primary objective in the design of such a systems is to provide the highest efficiency, which can be understood as a kind of tradeoff between accuracy and processing time. In order to achieve the goal a model of the system has to be adapted to the specificities. In the case of streaming data classification we should take into account that: (a) the characteristics of data may change over time, which is called Concept Drift, (b) the computational speed of the system must be high enough to allow efficient processing of large amounts of information in an acceptably short time. In the project we plan to develop number of algorithms which ensuring high resistance of classification system to aforementioned concept drift. It is plan to investigate possibility of application of algorithms using a distributed and parallel programming paradigms in order to ensure high processing speed of streaming data. Concluding, we define the following project objectives: 1. Developing new methods of supervised and unsupervised concept drift detection along with respective classification algorithms dedicated for stream processing; 2. Developing new classifier models along with respective adaptive learning algorithms aiming at permanent adjustment classifier parameters to changing characteristics of the data stream, especially ensemble of classifiers; 3. Developing machine learning algorithms using a distributed and parallel programming paradigms.