Machine Learning for Cognitive Networks: Technology Assessment and Research Challenges

Dietterich, Thomas G.; Langley, Pat

doi:10.1002/9780470515143.ch5

Cited by 55 publications

(54 citation statements)

References 27 publications

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“…In literature, multiple relations are combined either by using a set of neighbor class predictor variables per relation in a single local classifier [20,22] or by merging the relations and summing the weights of common links [16]. Here, we will present a new approach of combining different relations using ensemble classification [5].…”

Section: Introductionmentioning

confidence: 99%

Relational Ensemble Classification

Preisach

Schmidt-Thieme

2006

Sixth International Conference on Data Mining (ICDM'06)

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Section: Introductionmentioning

confidence: 99%

Relational Ensemble Classification

Preisach

Schmidt-Thieme

2006

Sixth International Conference on Data Mining (ICDM'06)

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“…• Third experiment: majority voting [21]. Once the list of labels for each test case is obtained by each classifier, the multiclassifier will predict the label, y', of each test case, x, based on the majority label; the tie case is solved randomly, providing an arbitrary solution.…”

Section: Multiclassifier Approachmentioning

confidence: 99%

A Multiclassifier Approach for Drill Wear Prediction

Dı́ez

Carrascal

2012

Machine Learning and Data Mining in Pattern Recognition

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Abstract. Classification methods have been widely used during last years in order to predict patterns and trends of interest in data. In present paper, a multiclassifier approach that combines the output of some of the most popular data mining algorithms is shown. The approach is based on voting criteria, by estimating the confidence distributions of each algorithm individually and combining them according to three different methods: confidence voting, weighted voting and majority voting. To illustrate its applicability in a real problem, the drill wear detection in machine-tool sector is addressed. In this study, the accuracy obtained by each isolated classifier is compared with the performance of the multiclassifier when characterizing the patterns of interest involved in the drilling process and predicting the drill wear. Experimental results show that, in general, false positives obtained by the classifiers can be slightly reduced by using the multiclassifier approach.

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“…The new methodology does not require any a priori knowledge about the symbiotic service influences on the network. Being a form of machine learning [3] [4], the Least Square Policy Iteration (LSPI) [5] algorithm gathers knowledge through a number of trial-and-error episodes. It uses basis functions, features from the network, to make an assessment about the influence that different service sets pose on each network incentive.…”

Section: Introductionmentioning

confidence: 99%

A reinforcement learning based solution for cognitive network cooperation between co-located, heterogeneous wireless sensor networks

et al. 2014

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Due to a drastic increase of the number of wireless communication devices, these devices are forced to interfere or interact with each other. This raises the issue of possible effects this coexistence might have on the performance of each of the networks. Negative effects are a consequence of contention for network resources (such as free wireless communication frequencies) between different devices. On the other hand, a possible cooperation between co-located networks could also improve certain aspects of networking for each one of them. This paper presents a self-learning, cognitive cooperation approach for heterogeneous co-located networks. Enabling cooperation is performed by activating or deactivating services that influence the interaction between wireless devices, such as an interference avoidance service, a packet sharing service, etc. Activation of a cooperative service might have both positive and negative effects on network's performance, regarding its high level goals. Such a cooperation approach has to incorporate a reasoning mechanism, centralized or distributed, able to determine the influence of each symbiotic service on the performance of all the participating sub-networks, taking into consideration their requirements. Coupled with the concept of enabling symbiotic services, a machine learning technique known as the Least Squares Policy Iteration (LSPI), is presented in this paper as a novel network cooperation paradigm.

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Machine Learning for Cognitive Networks: Technology Assessment and Research Challenges

Cited by 55 publications

References 27 publications

Relational Ensemble Classification

Relational Ensemble Classification

A Multiclassifier Approach for Drill Wear Prediction

A reinforcement learning based solution for cognitive network cooperation between co-located, heterogeneous wireless sensor networks

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