Improving the performance of an incremental algorithm driven by error margins

Campo-Ávila, José del; Ramos-Jiménez, Gonzalo; Gama, João; Morales-Bueno, Rafael

doi:10.3233/ida-2008-12305

Cited by 10 publications

(9 citation statements)

References 18 publications

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“…In IADEM [58] and IADEMc [59] the tree construction is also based on the Hoeffding bound. The tree growth is stopped using the error rate given as a parameter to the algorithm.…”

Section: Main Algorithms In the Literaturementioning

confidence: 99%

Business Intelligence

Zimnyi¹,

Kutsche²

2015

Lecture Notes in Business Information Processing

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Abstract. The last ten years were prolific in the statistical learning and data mining field and it is now easy to find learning algorithms which are fast and automatic. Historically a strong hypothesis was that all examples were available or can be loaded into memory so that learning algorithms can use them straight away. But recently new use cases generating lots of data came up as for example: monitoring of telecommunication network, user modeling in dynamic social network, web mining, etc. The volume of data increases rapidly and it is now necessary to use incremental learning algorithms on data streams. This article presents the main approaches of incremental supervised classification available in the literature. It aims to give basic knowledge to a reader novice in this subject.

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“…In IADEM [58] and IADEMc [59] the tree construction is also based on the Hoeffding bound. The tree growth is stopped using the error rate given as a parameter to the algorithm.…”

Section: Main Algorithms In the Literaturementioning

confidence: 99%

Business Intelligence

Zimnyi¹,

Kutsche²

2015

Lecture Notes in Business Information Processing

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“…In the last few years, the need to extract novel information from data streams has led to the design of different incremental learning architectures that can create data models online as new examples are coming to the system (Aggarwal 2007;del Campo-Á vila et al 2008;Gama and Gaber 2007). This type of learning has received especial attention not only because it enables practitioners to extract key information from problems in which data are continuously generated and where concepts may change over time, e.g., stock market and sensor data among others, but also because it enables them to deal with huge data sets by making them available as data streams.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy knowledge representation study for incremental learning in data streams and classification problems

Orriols-Puig

Casillas

2010

Soft Comput

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The extraction of models from data streams has become a hot topic in data mining due to the proliferation of problems in which data are made available online. This has led to the design of several systems that create data models online. A novel approach to online learning of data streams can be found in Fuzzy-UCS, a young Michiganstyle fuzzy-classifier system that has recently demonstrated to be highly competitive in extracting classification models from complex domains. Despite the promising results reported for Fuzzy-UCS, there still remain some hot issues that need to be analyzed in detail. This paper carefully studies two key aspects in Fuzzy-UCS: the ability of the system to learn models from data streams where concepts change over time and the behavior of different fuzzy representations. Four fuzzy representations that move through the dimensions of flexibility and interpretability are included in the system. The behavior of the different representations on a problem with concept changes is studied and compared to other machine learning techniques prepared to deal with these types of problems. Thereafter, the comparison is extended to a large collection of realworld problems, and a close examination of which problem characteristics benefit or affect the different representations is conducted. The overall results show that Fuzzy-UCS can effectively deal with problems with concept changes and lead to different interesting conclusions on the particular behavior of each representation.

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“…To solve the problem, various research work used interval estimation [5,8,6,9]. These approaches rank the possible splits by using a split evaluation function, and the best split is selected when an adequate confidence level can be guaranteed.…”

Section: Introductionmentioning

confidence: 99%

“…A higher confidence level used for splitting can provide, with higher probability, a decision tree model that is more similar to that one induced from the entire data stream. Several approaches have computed confidence intervals making no assumption regarding the form of the probability distribution of the input data [5,8,6,9].…”

Section: Introductionmentioning

confidence: 99%

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An Online Tree-Based Approach for Mining Non-Stationary High-Speed Data Streams

Díaz

Blanco²,

Mariño

et al. 2020

RITA

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This paper presents a new learning algorithm for inducing decision trees from data streams. In these domains, large amounts of data are constantly arriving over time, possibly at high speed. The proposed algorithm uses a top-down induction method for building trees, splitting leaf nodes recursively, until none of them can be expanded. The new algorithm combines two split methods in the tree induction. The first method is able to guarantee, with statistical significance, that each split chosen would be the same as that chosen using infinite examples. By doing so, it aims at ensuring that the tree induced online is close to the optimal model. However, this split method often needs too many examples to make a decision about the best split, which delays the accuracy improvement of the online predictive learning model. Therefore, the second method is used to split nodes more quickly, speeding up the tree growth. The second split method is based on the observation that larger trees are able to store more information about the training examples and to represent more complex concepts. The first split method is also used to correct splits previously suggested by the second one, when it has sufficient evidence. Finally, an additional procedure rebuilds the tree model according to the suggestions made with an adequate level of statistical significance. The proposed algorithm is empirically compared with several well-known induction algorithms for learning decision trees from data streams. In the tests it is possible to observe that the proposed algorithm is more competitive in terms of accuracy and model size using various synthetic and real world datasets.

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Improving the performance of an incremental algorithm driven by error margins

Cited by 10 publications

References 18 publications

Business Intelligence

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Fuzzy knowledge representation study for incremental learning in data streams and classification problems

An Online Tree-Based Approach for Mining Non-Stationary High-Speed Data Streams

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