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1

An empirical study of a simple incremental classifier based on vector quantization and adaptive resonance theory

Czmil Sylwester, Kluska Jacek, Czmil Anna

International Journal of Applied Mathematics and Computer Science

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2024

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Vol. 34, no. 1

149--165

EN

When constructing a new data classification algorithm, relevant quality indices such as classification accuracy (ACC) or the area under the receiver operating characteristic curve (AUC) should be investigated. End-users of these algorithms are interested in high values of the metrics as well as the proposed algorithm’s understandability and transparency. In this paper, a simple evolving vector quantization (SEVQ) algorithm is proposed, which is a novel supervised incremental learning classifier. Algorithms from the family of adaptive resonance theory and learning vector quantization inspired this method. Classifier performance was tested on 36 data sets and compared with 10 traditional and 15 incremental algorithms. SEVQ scored very well, especially among incremental algorithms, and it was found to be the best incremental classifier if the quality criterion is the AUC. The Scott–Knott analysis showed that SEVQ is comparable in performance to traditional algorithms and the leading group of incremental algorithms. The Wilcoxon rank test confirmed the reliability of the obtained results. This article shows that it is possible to obtain outstanding classification quality metrics while keeping the conceptual and computational simplicity of the classification algorithm.

2

Video streaming service identification using incremental learning on software-defined network

Castañeda Herrera Luis Miguel, Campo Muñoz Wilmar Yesid, Duque-Torres Alejandra

Przegląd Elektrotechniczny

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2022

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R. 98, nr 8

89--94

EN

Software-defined networking (SDN) has emerged as a solution to the management challenges facing data networks today, including the identification of different types of services. Therefore, in this paper we present the classification of video streaming in SDN environments. Since, SDN enables the collection and extraction of patterns from traffic flows, through incremental ML algorithms to use classification models that identify video streaming. The results demonstrates that we can identify online video streaming traffic using the Adaptive Random Forest model (ARF).

PL

Sieć definiowana programowo (SDN) pojawiła się jako rozwiązanie problemów związanych z zarządzaniem, z jakimi borykają się współczesne sieci danych, w tym z identyfikacją różnych rodzajów usług. Dlatego w niniejszym artykule przedstawiamy klasyfikację strumieniowania wideo w środowiskach SDN. Ponieważ SDN umożliwia zbieranie i wyodrębnianie wzorców z przepływów ruchu za pomocą przyrostowych algorytmów ML w celu wykorzystania modeli klasyfikacji, które identyfikują strumieniowanie wideo. Wyniki pokazują, że możemy zidentyfikować ruch strumieniowy wideo online za pomocą modelu Adaptive Random Forest (ARF).

3

Przegląd metod uczenia inkrementacyjnego

Blachnik M., Wieczorek T.

Studia Informatica

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2015

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Vol. 36, nr 1

47--60

EN

Classical training methods of computational intelligence models are based on building a knowledge base, assuming that the entire, complete set of learning vectors is available. This assumption is not always met, particularly in issues related to the industry. In the paper we provide an overview of a broad group of algorithms supporting incremental learning which includes: case based on reasoning, kernel methods, and incremental induction of rule-based systems.

PL

Klasyczne metody uczenia modeli inteligencji obliczeniowej opierają się na budowaniu bazy wiedzy, przy założeniu że dostępny jest cały, skończony zbiór przypadków uczących. Założenie to nie zawsze jest spełnione, dlatego też w artykule dokonano przeglądu różnych metod uczenia z możliwością douczania modelu predykcyjnego w miarę napływu nowych danych uczących. Omówiono także metody z rodziny: wnioskowania na podstawie przypadków, modeli bazujących na funkcjach jądrowych oraz systemów regułowych.

4

Incremental Computing Approximations with the Dynamic Object set in Interval-valued Ordered Information System

Yu J., Xu W.

Fundamenta Informaticae

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2015

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Vol. 142, nr 1-4

373--397

EN

Rough set theory has been successfully used in formation system for classification analysis and knowledge discovery. The upper and lower approximations are fundamental concepts of this theory. The new information arrives continuously and redundant information may be produced with the time in real-world application. So, then incremental learning is an efficient technique for knowledge discovery in a dynamic database, which enables acquiring additional knowledge from new data without forgetting prior knowledge, which need to be updated incrementally while the object set get varies over time in the interval-valued ordered information system. In this paper, we analyzed the updating mechanisms for computing approximations with the variation of the object set. Two incremental algorithms respectively for adding and deleting objects with updating the approximations are proposed in interval-valued ordered information system. Furthermore, extensive experiments are carried out on six UCI data sets to verify the performance of these proposed algorithms. And the experiments results indicate the incremental approaches significantly outperform non-incremental approaches with a dramatic reduction in the computational time.

5

Incremental Maintenance of Rough Fuzzy Set Approximations under the Variation of Object Set

Zeng A., Li T., Zhang J., Chen H.

Fundamenta Informaticae

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2014

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Vol. 132, nr 3

401--422

EN

The lower and upper approximations in rough set theory will change dynamically over time due to the variation of the information system. Incremental methods for updating approximations in rough set theory and its extensions have received much attention recently. Most existing incremental methods have difficulties in dealing with fuzzy decision systems which decision attributes are fuzzy. This paper introduces an incremental algorithm for updating approximations of rough fuzzy sets under the variation of the object set in fuzzy decision systems. In experiments on 6 data sets from UCI, comparisons of the incremental and non-incremental methods for updating approximations are conducted. The experimental results show that the incremental method effectively reduces the computational time.

6

Incremental rule-based learners for handling concept drift: an overview

Deckert M

Foundations of Computing and Decision Sciences

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2013

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Vol. 38, No. 1

35--65

EN

Learning from non-stationary environments is a very popular research topic. There already exist algorithms that deal with the concept drift problem. Among them there are online or incremental learners, which process data instance by instance. Their knowledge representation can take different forms such as decision rules, which have not received enough attention in learning with concept drift. This paper reviews incremental rule-based learners designed for changing environments. It describes four of the proposed algorithms: FLORA, AQ11-PM+WAH, FACIL and VFDR. Those four solutions can be compared on several criteria, like: type of processed data, adjustment to changes, type of the maintained memory, knowledge representation, and others.

7

A Robust Multiple Classifier System for Pixel Classification of Remote Sensing Images

Maulik U., Chakraborty D.

Fundamenta Informaticae

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2010

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Vol. 101, nr 4

286-304

EN

Satellite image classification is a complex process that may be affected by many factors. This article addresses the problem of pixel classification of satellite images by a robust multiple classifier system that combines k-NN, support vector machine (SVM) and incremental learning algorithm (IL). The effectiveness of this combination is investigated for satellite imagery which usually have overlapping class boundaries. These classifiers are initially designed using a small set of labeled points. Combination of these algorithms has been done based on majority voting rule. The effectiveness of the proposed technique is first demonstrated for a numeric remote sensing data described in terms of feature vectors and then identifying different land cover regions in remote sensing imagery. Experimental results on numeric data as well as two remote sensing data show that employing combination of classifiers can effectively increase the accuracy label. Comparison is made with each of these single classifiers in terms of kappa value, accuracy, cluster quality indices and visual quality of the classified images.

8

RRIA : A Rough Set and Rule Tree Based Incremental Knowledge Acquisition Algorithm

Zheng Z., Wang G.

Fundamenta Informaticae

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2004

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Vol. 59, nr 2,3

299--313

EN

As a special way in which the human brain is learning new knowledge, incremental learning is an important topic in AI. It is an object of many AI researchers to find an algorithm that can learn new knowledge quickly, based on original knowledge learned before, and in such way that the knowledge it acquires is efficient in real use. In this paper, we develop a rough set and rule tree based incremental knowledge acquisition algorithm. It can learn from a domain data set incrementally. Our simulation results show that our algorithm can learn more quickly than classical rough set based knowledge acquisition algorithms, and the performance of knowledge learned by our algorithm can be the same as or even better than classical rough set based knowledge acquisition algorithms. Besides, the simulation results also show that our algorithm outperforms ID4 in many aspects.

9

Beta neuro-fuzzy systems

Alimi A. M.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

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2003

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Vol. 7, No 1

23-41

EN

In this paper we present the Beta function and its main properties. A key feature of the Beta function, which is given by the central-limit theorem, is also given. We then introduce a new category of neural networks based on a new kernel: the Beta function. Next, we investigate the use of Beta fuzzy basis functions for the design of fuzzy logic systems. The functional equivalence between Beta-based function neural networks and Beta fuzzy logic systems is then shown with the introduction of Beta neuro-fuzzy systems. By using the SW theorem and expanding the output of the Beta neuro-fuzzy system into a series of Beta fuzzy-based functions, we prove that one can uniformly approximate any real continuous function on a compact set to any arbitrary accuracy. Finally, a learning algorithm of the Beta neuro-fuzzy system is described and illustrated with numerical examples.

10

A Generalization Model Based on OI-implication for Ideal Theory Refinement

Esposito F., Fanizzi N., Ferilli S., Semeraro G.

Fundamenta Informaticae

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2001

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Vol. 47, nr 1,2

15-33

EN

A framework for theory refinement is presented pursuing the efficiency and effectiveness of learning regarded as a search process. A refinement operator satisfying these requirements is formally defined as ideal. Past results have demonstrated the impossibility of specifying ideal operators in search spaces where standard generalization models, like logical implication or q-subsumption, are adopted. By assuming the object identity bias over a space defined by a clausal language ordered by logical implication, a novel generalization model, named OI-implication, is derived and we prove that ideal operators can be defined for the resulting search space.