Online learning classifiers in dynamic environments with incomplete feedback

Behdad, Mohammad; French, Tim

doi:10.1109/cec.2013.6557777

Cited by 6 publications

(8 citation statements)

References 11 publications

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“…We convey the frequent properties of fraud, which can be tuned to display those characteristics. We report experiments on this theoretical problem with a accepted real time learning classier system algorithm [18]. The results from our experiments indicate that this method can prevail over the difficulties inherent to the fraud detection problem.…”

Section: Different Techniques For Fraud Detectionmentioning

confidence: 90%

“…The results from our experiments indicate that this method can prevail over the difficulties inherent to the fraud detection problem. At the end, when we apply the e algorithm to a real world problem (KDD Cup 1999 network intrusion detection), then we show that it can realize that we gain very good result in this domain [18].…”

Section: Different Techniques For Fraud Detectionmentioning

confidence: 99%

“…The paper vitally appraises the risk based method and discusses its applicability in practice [17]. Fraud is a very harsh problem that expenditure the global economy trillions of dollars annually [18]. So, fraud detection is difficulties as perpetrators keenly attempt to wrap up their actions.…”

Section: Different Techniques For Fraud Detectionmentioning

confidence: 99%

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Survey on Fraud Detection Techniques Using Data Mining

Arif¹,

Dar²

2015

IJUNESST

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Section: Different Techniques For Fraud Detectionmentioning

confidence: 90%

Section: Different Techniques For Fraud Detectionmentioning

confidence: 99%

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Survey on Fraud Detection Techniques Using Data Mining

Arif¹,

Dar²

2015

IJUNESST

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“…However, the ability to trigger population re-initialization is a function of prior knowledge regarding what constitutes a 'good' threshold. Behdad and French [13] note that under batch learning scenarios, LCS are first deployed under a purely exploratory setting and then under a purely exploitative setting. Such a separation might not be appropriate in the case of online learning.…”

Section: Learning Classifier Systems and Prototype Methodsmentioning

confidence: 99%

“…Evolve: [3,71,125,143,159] Non-evolve: [169,185,192] Class imbalance (3.3.3, 5.6) Evolve: [7,8,155,175] Non-evolve: [84,142] Active learning (5.1.2) Evolve: [7,8,175] Non-evolve: [86,97,127,199,202,203] Change detection (5.3) Input: [4,19,44,53,73,105,160,169,181] Model (GP): [125,155] Model (non-GP): [12,38,78,117,118,123,124,144,199,[201][202][203] Label budgets (5.3) Evolve: [13,175] Non-evolve: [121,…”

Section: 33)mentioning

confidence: 99%

Evolutionary model building under streaming data for classification tasks: opportunities and challenges

Heywood

2014

Genet Program Evolvable Mach

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Streaming data analysis potentially represents a significant shift in emphasis from schemes historically pursued for offline (batch) approaches to the classification task. In particular, a streaming data application implies that: (1) the data itself has no formal 'start' or 'end'; (2) the properties of the process generating the data are non-stationary, thus models that function correctly for some part(s) of a stream may be ineffective elsewhere; (3) constraints on the time to produce a response, potentially implying an anytime operational requirement; and (4) given the prohibitive cost of employing an oracle to label a stream, a finite labelling budget is necessary. The scope of this article is to provide a survey of developments for model building under streaming environments from the perspective of both evolutionary and non-evolutionary frameworks. In doing so, we bring attention to the challenges and opportunities that developing solutions to streaming data classification tasks are likely to face using evolutionary approaches.

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Towards a Parallel Computationally Efficient Approach to Scaling Up Data Stream Classification

Tennant

Stahl

Fatta

et al. 2014

Research and Development in Intelligent Systems XXXI

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2014)Towards a parallel computationally efficient approach to scaling up data stream classification.Abstract Advances in hardware technologies allow to capture and process data in real-time and the resulting high throughput data streams require novel data mining approaches. The research area of Data Stream Mining (DSM) is developing data mining algorithms that allow us to analyse these continuous streams of data in realtime. The creation and real-time adaption of classification models from data streams is one of the most challenging DSM tasks. Current classifiers for streaming data address this problem by using incremental learning algorithms. However, even so these algorithms are fast, they are challenged by high velocity data streams, where data instances are incoming at a fast rate. This is problematic if the applications desire that there is no or only a very little delay between changes in the patterns of the stream and absorption of these patterns by the classifier. Problems of scalability to Big Data of traditional data mining algorithms for static (non streaming) datasets have been addressed through the development of parallel classifiers. However, there is very little work on the parallelisation of data stream classification techniques. In this paper we investigate K-Nearest Neighbours (KNN) as the basis for a real-time adaptive and parallel methodology for scalable data stream classification tasks. ety (possible unstructured format) of 'Big Data'. Traditional data mining methods for classification of static data take several passes through the training data in order to generate the classification model, which is then applied on previously unseen data instances. Streaming models differ from this learning procedure of Train and Test to a system that continuously needs to be evaluated and updated. As the data is often either too fast to process in depth, or too vast to store, data stream classifiers must be naturally incremental, adaptive and responsive to single exposures to data instances. The continuous task of re-learning and adaptation aims to tackle the problem of concept drift [12] (changes of the patterns encoded in the streams over time). An ideal data stream classifier should incorporate certain features [18]: the classifier must limit its size (memory footprint) as streams are theoretically infinitely long; the time taken to process each instance is short and constant so as not to create a bottleneck; each data instance is only seen once by the classifier; the classification model created incrementally should be equivalent to a 'batch' learner given the same training data; and the classifier must be able to handle concept drift. Data streams come in all forms as technologies merge and become more interconnected. Classic applications are: sensor networks; Internet traffic management and web log analysis [13]; TCP/IP packet monitoring [8]; and intrusion detection [15]. However, capturing, storing and processing these data streams is not feasible, as the data stream is potentially infinite. Systems...

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Online learning classifiers in dynamic environments with incomplete feedback

Cited by 6 publications

References 11 publications

Survey on Fraud Detection Techniques Using Data Mining

Survey on Fraud Detection Techniques Using Data Mining

Evolutionary model building under streaming data for classification tasks: opportunities and challenges

Towards a Parallel Computationally Efficient Approach to Scaling Up Data Stream Classification

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