
Agnieszka JastrzebskaWarsaw University of Technology · Faculty of Mathematics and Information Science
Agnieszka Jastrzebska
PhD DSc
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Publications (99)
This paper proposes an algorithm called Forward Composition Propagation (FCP) to explain the predictions of feed-forward neural networks operating on structured classification problems. In the proposed FCP algorithm, each neuron is described by a composition vector indicating the role of each problem feature in that neuron. Composition vectors are...
The chapter presents an FCM-based model for pattern classification termed Long-Term Cognitive Network (LTCN). This model uses the class-per-output architecture discussed in the previous chapter and the quasi-nonlinear reasoning rule to avoid the unique fixed-point attractor. To improve its prediction capabilities, the LTCN-based classifier suppress...
This chapter describes an FCM model for decision-making and prediction problems where concepts are split into inputs and outputs. A key property of this model relies on its hybrid nature, where experts are expected to define the weights of some relationships while others are learned from the data. The learning procedure does not alter the weights d...
In this chapter, we elaborate on the construction of a FCM-based classifier for tabular data classification. The pipeline comprises exploratory data analysis, preliminary input processing, classification mechanism construction, and quality evaluation. The specifics of how to adapt an FCM to this task are discussed. We use a two-block FCM architectu...
The case study presented in this book highlights the properties and challenges of distinguishing bot and human traffic using weblogs and compares several solutions to this task. We present here, first, the observations related to the methodological side of the potential problem solution.
When dealing with challenging, real-world datasets, one may turn to hybrid data processing approaches that join several kinds of data analysis algorithms. The hybrid data analysis techniques are popular in the literature, where we may find fusions of optimization and classification algorithms, clustering and classification algorithms.
In this chapter, we will look in a more detail at the ways in which data were acquired and processed in the framework of the project in question. We will do so against the background of observations and examples already provided in the preceding section, starting with the first section of the present chapter.
Having characterised the problem that we address and the way of acquiring data that we use, we now turn to the features, which can be extracted from the raw data, and the choice of the possibly good selection of a subset of these variables from the point of view of the problem at hand.
We start with the description of general context and with formulation of the problem that we address. In this manner we set a framework for both the particular issues that we deal with on a technical level, described in the consecutive parts of the book, and for the potential implications thereof, some of them forwarded as more general conclusions...
Classification is a decision-making problem, in which we aim at the assignment of a correct class label to an observation. In the typical scenario, the set of available class labels is fixed beforehand and remains unchanged. Advanced data processing streams allow for a more flexible definition of this task. Typically, they admit the existence of a...
This chapter is devoted to presentation of results, which were obtained for the data considered with the use of clustering algorithms. Clustering consists in grouping of similar observations, while separating the dissimilar ones. (The groups obtained therefrom are referred to, exactly, as clusters.) Hence, we might suspect that the observations we...
Analyzing data from the web is now one of the primary tasks, understood in a variety of manners and solved for a very wide variety of purposes. The talk describes the experience from a project, devoted to analyzing such data while drawing some more general conclusions. The project was aimed at distinguishing artificial ad-related traffic from the g...
The paper presents a new method to detect economic growth regimes based on time-series similarity analysis. The procedure builds upon a fuzzy concept-based model, the Jaccard Index, and a hierarchical clustering procedure. Having introduced the proposed method, we apply it to the time series of economic growth and its proximate factors. Further, we...
One drawback of using the existing one-step forecasting models for long-term time series prediction is the cumulative errors caused by iterations. In order to overcome this shortcoming, this article proposes a trend-fuzzy-granulation-based adaptive fuzzy cognitive map (FCM) for long-term time series forecasting. Different from the original FCM-base...
This paper presents a Prolog-based reasoning module to generate counterfactual explanations given the predictions computed by a black-box classifier. Our approach comprises four well-defined stages that can be applied to any structured pattern classification problem. Firstly, we pre-process the given dataset by imputing missing values and normalizi...
Dictionary-based classifiers are an essential group of approaches in the field of time series classification. Their distinctive characteristic is that they transform time series into segments made of symbols (words) and then classify time series using these words. Dictionary-based approaches are suitable for datasets containing time series of unequ...
The article analyses the similarity of business cycles
among European Union countries. For this purpose, we design
a new method – a fuzzy concept-based model, operating on time series windows. We showcase the capability of the tool to detect
similarities in the phase and the amplitude of the cycle, and we
compare it with the commonly used measure o...
Time series classification is a supervised learning problem that aims at labelling time series according to their class belongingness. Time series can be of variable length. Many algorithms have been proposed, among which feature-based approaches play a key role, but not all of them are able to deal with time series of unequal lengths. In this pape...
Forecasting windmill time series is often the basis of other processes such as anomaly detection, health monitoring, or maintenance scheduling. The amount of data generated by windmill farms makes online learning the most viable strategy to follow. Such settings require retraining the model each time a new batch of data is available. However, updat...
In this paper, we present a recurrent neural system named long short-term cognitive networks (LSTCNs) as a generalization of the short-term cognitive network (STCN) model. Such a generalization is motivated by the difficulty of forecasting very long time series efficiently. The LSTCN model can be defined as a collection of STCN blocks, each process...
Time series processing is an essential aspect of wind turbine health monitoring. In this paper, we propose two new approaches for analyzing wind turbine health. Both methods are based on abstract concepts, implemented using fuzzy sets, which allow aggregating and summarizing the underlying raw data in terms of relative low, moderate, and high power...
The relationship between income and growth rates has been an elementary problem of research on economic convergence. In the present paper, we study growth-income paths in a new perspective. We assess the similarities of transitional growth trajectories with the use of novel concept-based model. Further, we group economies on the basis of the assess...
Online advertising campaigns are adversely affected by bot traffic. In this paper, we develop and test a method for the estimation of its share, which is necessary for the evaluation of campaign efficiency. First, we present the nature of the problem as well as the underlying business rationale. Next, we describe the essential features of Internet...
Multivariate time series classification is a machine learning problem that can be applied to automate a wide range of real-world data analysis tasks. ROCKET proved to be an outstanding algorithm capable to classify time series accurately and quickly. The textbook variant of the multivariate time series classification problem assumes that time serie...
A fuzzy cognitive map (FCM) is a graph-based knowledge representation model wherein the connections of the nodes (edges) represent casual relationships between the knowledge items associated with the nodes. This model has been applied to solve various modeling tasks including forecasting time series. In the original FCM-based forecasting model, cau...
This paper presents a Prolog-based reasoning module to generate counterfactual explanations given the predictions computed by a black-box classifier. The proposed symbolic reasoning module can also resolve what-if queries using the ground-truth labels instead of the predicted ones. Overall, our approach comprises four well-defined stages that can b...
This article presents a comprehensive approach for time-series classification. The proposed model employs a fuzzy cognitive map (FCM) as a classification engine. Preprocessed input data feed the employed FCM. Map responses, after a postprocessing procedure, are used in the calculation of the final classification decision. The time-series data are s...
Time series processing is an essential aspect of wind turbine health monitoring. Despite the progress in this field, there is still room for new methods to improve modeling quality. In this paper, we propose two new approaches for the analysis of wind turbine health. Both approaches are based on abstract concepts, implemented using fuzzy sets, whic...
Time series similarity evaluation is a crucial processing task performed either as a stand-alone action or as a part of extensive data analysis schemes. Among essential procedures that rely on measuring time series similarity, we find time series clustering and classification. While the similarity of regular (not temporal) data frames is studied ex...
In this paper, we study economic growth and its volatility from an episodic perspective. We first demonstrate the ability of the genetic algorithm to detect shifts in the volatility and levels of a given time series. Having shown that it works well, we then use it to detect structural breaks that segment the GDP per capita time series into episodes...
Forecasting windmill time series is often the basis of other processes such as anomaly detection, health monitoring, or maintenance scheduling. The amount of data generated on windmill farms makes online learning the most viable strategy to follow. Such settings require retraining the model each time a new batch of data is available. However, updat...
In this paper, we present a recurrent neural system named Long Short-term Cognitive Networks (LSTCNs) as a generalisation of the Short-term Cognitive Network (STCN) model. Such a generalisation is motivated by the difficulty of forecasting very long time series in an efficient, greener fashion. The LSTCN model can be defined as a collection of STCN...
This paper presents an interpretable neural system-termed Evolving Long-term Cognitive Network-for pattern classification. The proposed model was inspired by Fuzzy Cognitive Maps, which are interpretable recurrent neural networks for modeling and simulation. The network architecture is comprised of two neural blocks: a recurrent input layer and an...
In this paper, we look closely at the issue of contaminated data sets, where apart from legitimate (proper) patterns we encounter erroneous patterns. In a typical scenario, the classification of a contaminated data set is always negatively influenced by garbage patterns (referred to as foreign patterns). Ideally, we would like to remove them from t...
Time series classification is a thriving area of research in machine learning. Among many applications, it is frequently applied to human activity analysis. Time series describing a human in motion are ubiquitously collected via omnipresent mobile devices and can be subjected to further processing. In this paper, we propose a novel, deep learning a...
Hybrid artificial intelligence deals with the construction of intelligent systems by relying on both human knowledge and historical data records. In this paper, we approach this problem from a neural perspective, particularly when modeling and simulating dynamic systems. Firstly, we propose a Fuzzy Cognitive Map architecture in which experts are re...
In this paper, a new approach to time series classification is proposed. It transforms the scalar time series into a two-dimensional space of amplitude (time series values) and a change of amplitude (increment). Subsequently, it uses this representation to plot the data. One figure is produced for each time series. In consequence, the time series c...
The article presents a time series classification method based on Fuzzy Cognitive Maps. We advocate that Fuzzy Cognitive Maps provide a sound representation of time series and we can construct a classification mechanism based on them. The classifier has to distinguish maps constructed for time series belonging to different classes. The proposed cla...
In the paper we discuss the issue of contaminated data sets that contain improper patterns apart from proper ones. To distinguish between those two kinds of patterns we use terms: native (proper) patterns and foreign (garbage) patterns. To deal with contaminated datasets we propose to build decision mechanism based on a collection of classification...
We raise a few issues regarding time series modeling using Cognitive Maps, which is an example of a qualitative rather than a purely quantitative approach. Methods that operate at the level of concepts instead of numerical values are a worthy alternative for time series processing thanks to certain desirable properties such as abstraction and ease...
In the paper, we address a common issue of contaminated datasets, in which apart from proper native patterns we encounter garbage (foreign patterns). The required action is to remove foreign patterns. We propose a geometrical model for discrimination between native and foreign patterns. The idea is to enclose native patterns in elementary figures t...
In this article we address the problem of contaminated data in pattern recognition tasks, where apart from native patterns we may have foreign ones that do not belong to any native class. We present a novel approach to image classification with foreign pattern rejection based on cellular automata. The method is based only on native patterns, so no...
The study deals with an issue of recognition of native (proper) patterns and rejection of foreign (erroneous) patterns. We present a novel unsupervised approach to rejecting foreign patterns. We construct a geometrical model, which identifies regions in the feature space that are predominantly occupied by native patterns and determines regions wher...
This study presents an approach to time series modeling with Fuzzy Cognitive Maps. In the paper we focus on initial modeling phase: map nodes selection. The research objective was to introduce algorithmic means to evaluate Fuzzy Cognitive Map design before training phase. We posed a hypothesis that application of cluster validity indexes could serv...
In this study, we present an approach to multi-criteria decision-making modeling inspired by human cognitive processes. The proposed model exploits the ideas of fuzzy sets, balanced fuzzy sets and their connectives, namely t-norms, t-conorms and derived connectives. Balanced connectives are compared and contrasted with unipolar fuzzy connectives. O...
The motivation of our study is to provide algorithmic appro-aches to distinguish proper patterns, from garbage and erroneous patterns in a pattern recognition problem. The design assumption is to provide methods based on proper patterns only. In this way the approach that we propose is truly versatile and it can be adapted to any pattern recognitio...
Fuzzy Cognitive Maps (FCMs) are a framework based on weighted directed graphs which can be used for system modeling. The relationships between the concepts are stored in graph edges and they are expressed as real numbers from the \([-1,1]\) interval (called weights). Our goal was to evaluate the effectiveness of non-deterministic optimization algor...
In this paper pattern recognition problem with rejecting option is discussed. The problem is aimed at classification patterns from given classes (native patterns) and rejecting ones not belonging to these classes (foreign patterns). In practice the characteristics of the native patters are given, while no information about foreign ones is known. A...
The paper is focused on automated knowledge discovery in musical pieces, based on transformations of digital musical notation. Usually a single musical piece is analyzed, to discover the structure as well as traits of separate voices. Melody and rhythm is processed with the use of three proposed operators, that serve as meta-data. In this work we f...
The paper presents a novel approach to classification reinforced with rejection mechanism. The method is based on a two-tier set of classifiers. First layer classifies elements, second layer separates native elements from foreign ones in each distinguished class. The key novelty presented here is rejection mechanism training scheme according to the...
The motivation for undertaking the research on relations, such as complementarity, is complexity of real-world phenomena. Authors aim at preparation of proper modeling tools that would enable to describe dependencies to the greatest extent. The objective of this article is to discuss relation of complementarity. Authors propose definition of comple...
The article is focused on a particular aspect of classification, namely the imbalance of recognized classes. The paper contains a comparative study of results of musical symbols classification using known algorithms: k-nearest neighbors, k-means, Mahalanobis minimal distance, and decision trees. Authors aim at addressing the problem of imbalanced p...
The paper introduces definitions of exclusion relations in spaces of features and concepts. Concepts correspond to phenomena and they are described with their features. The objective of our research is to investigate and describe possible structuring and relations in the feature and concept spaces. In this article, three types of exclusions: weak,...
The article is focused on a particular aspect of classification, namely the issue of class imbalance. Imbalanced data adversely affects the recognition ability and requires proper classifier’s construction. In this work we present a case of music notation as an example of imbalanced data. Three classification algorithms - random forest, standard SV...
Frequently it happens that during symbols recognition, not all of them are the proper ones. This may cause deterioration of a classifying process. In this paper we present a way to “separate the wheat from the chaff”, by constructing a rejector, based on geometrical figures enclosing “wheat” and excluding “chaff”. We assume that entities of wheat,...
The article presents an application of fuzzy sets with triangular norms and balanced fuzzy sets with balanced norms to decision making modelling. We elaborate on a vector-based method for decision problem representation, where each element of a vector corresponds to an argument analysed by a decision maker. Vectors gather information that influence...
The article analyzes consecutive phases of time series modelling with Fuzzy Cognitive Maps. The subject of interest are features determining models of good quality. First, we present the procedure: design phase, learning phase, and in the end - application. The discussion is illustrated with experiments on two synthetic time series. We have shown t...
Classification, especially in the case of a small space of features, is prone to errors. This is more important when it is costly to gain data from samples to calculate the values for futures. We study what effect limiting the space of features has on the performance of built classifiers and how the quality of classification can be improved by reje...
This study is concerned with a fundamental issue of time series representation for modeling and prediction with Fuzzy Cognitive Maps. We introduce two distinct time series representation schemes for Fuzzy Cognitive Map design. First method is based on time series amplitude, amplitude change, and change of amplitude change (dynamics perspective). Se...
The paper is focused on man-machine communication, which is perceived in terms of data exchange. Understanding of data being exchanged is the fundamental property of intelligent communication. The main objective of this paper is to introduce the paradigm of intelligent data understanding. The paradigm stems from syntactic and semantic characterizat...
The paper presents a knowledge-driven homophony harmonization model for tonal music. Automatic harmonization could be seen as a complicated classification problem: an algorithm processes a music notation document and generates class labels (harmonic functions). The proposed model could be seen as an Expert System, based largely on the music theory....
This study elaborates on a comprehensive design methodology of Fuzzy Cognitive Maps (FCMs). Here the maps are regarded as a modeling vehicle of time series. It is apparent that whereas time series are predominantly numeric, FCMs are abstract constructs operating at the level of abstract entities referred to as concepts and represented by the indivi...
The article introduces three concepts’ rejection/selection criteria for Fuzzy Cognitive Map-based method of time series modeling and prediction. Proposed criteria are named entropy index, membership index and global distance index. Concepts’ selection strategies facilitate Fuzzy Cognitive Map design procedure. Proposed criteria allow to simplify, o...
In the article we have discussed an approach to time series modelling based on Fuzzy Cognitive Maps (FCMs). We have introduced FCM design method that is based on replicated ordered time series data points. We named this representation method history h, where h is number of consecutive data points we gather. Custom procedure for concepts/nodes extra...
Standard assumption of pattern recognition problem is that processed elements belong to recognized classes. However, in practice, we are often faced with elements presented to recognizers, which do not belong to such classes. For instance, paper-to-computer recognition technologies (e.g. character or music recognition technologies, both printed and...
The article presents an application of fuzzy sets with triangular norms and balanced fuzzy sets with balanced norms to decision making modelling. We elaborate on a vector-based method for decision problem representation, where each element of a vector corresponds to an argument analysed by a decision maker. Vectors gather information that influence...
In the article we present a technique for time series modeling that joins concepts based Fuzzy Cognitive Map design with moving window approach. Proposed method first extracts concepts that generalize the underlying time series. Next, we form a map that consists of several layers representing consecutive time points. In each layer we place concepts...
The article is focused on the issue of complexity of Fuzzy Cognitive Maps designed to model time series. Large Fuzzy Cognitive Maps are impractical to use. Since Fuzzy Cognitive Maps are graph-based models, when we increase the number of nodes, the number of connections grows quadratically. Therefore, we posed a question how to simplify trained FCM...
The objective of this paper is to present methodology for similarity evaluation of structured spaces of sets inspired by human cognitive processes. In contrast to classical similarity relations, which can operate only within the same space, our method can be applied to separate spaces. Proposed formulas are designed to compare two families of sets...
Cognitive Maps are abstract knowledge representation framework, suitable to model complex systems. Cognitive Maps are visualized with directed graphs, where nodes represent phenomena and edges represent relationships. Granular Cognitive Maps are augmented Cognitive Maps, which use knowledge granules as information representation model. Conceptually...
Fuzzy Cognitive Maps are recognized knowledge modeling tool. FCMs are visualized with directed graphs. Nodes represent information, edges represent relations within information. The core element of each Fuzzy Cognitive Map is weights matrix, which contains evaluations of connections between map's nodes. Typically, weights matrix is constructed by e...
The article is focused on a particular aspect of classification, namely the imbalance of recognized classes. Imbalanced data adversely affects the recognition ability and requires proper classifier’s construction. The aim of presented study is to explore the capabilities of classifier combining methods with such raised problem. In this paper author...
The objective of this paper is to present developed methodology for Granular Cognitive Map reconstruction. Granular Cognitive Maps model complex imprecise systems. With a proper adjustment of granularity parameters, a Granular Cognitive Map can represent given system with good balance between generality and specificity of the description. The autho...
The paper is focused on fuzzy cognitive maps - abstract soft computing models, which can be applied to model complex systems with uncertainty. The authors present two distinct methodologies for fuzzy cognitive map reconstruction based on gradient learning. Both theoretical and practical issues involved in the process of a map reconstruction are dis...
The article discusses abstract spaces of concepts and features. Concepts correspond to real-world objects. Concepts are described by their features. The study is devoted to relations in the space of concepts and in the space of features. Of greatest interest is similarity of structures in the concepts and features spaces. There is a direct link bet...
The article is focused on consumer's needs modeling. Authors develop and describe a theoretical model based on Maslow's needs hierarchy. Presented approach allows to compare consumers, represented by vectors of needs. Consumers' preferences are described in the framework of fuzzy sets theory. Authors apply a measure of consumers' dissimilarity. We...
Consumer decision making processes are conditioned by various forces. Recognized premises are being constantly reevaluated and future decisions are made in connection with previous ones. Therefore, authors propose an approach to decision making modeling based on pairs of vectors describing attitudes towards certain attributes influencing consumer's...
This paper is focused on modeling consumer's behavior related to decision making with some models of imperfect information. Our approach is based on the Lewin's field theory with our own expansion stemming from it. We use a few imperfect information models, namely: fuzzy sets, triangular norms and balanced norms. The former two models are applied i...