A Diversified Classification Committee for Recognition of Innovative Internet Domains

Mirończuk, M.; Protasiewicz, Jarosław

doi:10.1007/978-3-319-34099-9_29

Cited by 12 publications

(9 citation statements)

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“…Thus, we can state that the classification is based on multiple sources. In our first research [47], we combined these sources and proposed a classification method to obtain satisfactory classification indicators. However, the simple classification, which utilised each data set separately, produced insufficient results.…”

Section: Related Work Vs Proposed Approachmentioning

confidence: 99%

“…it is the conjunction of several appropriately selected documents from the website. This representation is a concatenation of the n best documents from a given website as determined by the Okapi BM25 search system [33,44,47,57] (for more technical details, see Appendix A). The textual data from each data set is classified by an appropriate classification function.…”

Section: Innovative Website Recognition Systemmentioning

confidence: 99%

“…Simple diversified classification committee γ Voting Our previous experiments [47] were intended to compare the best NB classifiers (γ NB1 , γ NB2 , and γ NB3 ) with γ V oting . The γ V oting classification function is based on a simple voting process.…”

Section: Aligned Space Of Featuresmentioning

confidence: 99%

“…In our previous study [47], we proposed an experimental Naïve Bayes (NB) classification committee for categorisation of companies' websites into the innovative or noninnovative group. The committee was based on a voting idea and diversified feature spaces, feature weights, and two models of features distribution, i.e.…”

Section: Introductionmentioning

confidence: 99%

“…In the recent literature, we did not find any related works that would include the topics mentioned above. In this study, we propose a new classification committee, which is an extended version of our previous proposition [47]. The primary enhancement relies on the application of a stacked generalisation method instead of a simple voting procedure.…”

Section: Introductionmentioning

confidence: 99%

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Recognising innovative companies by using a diversified stacked generalisation method for website classification

Mirończuk

Protasiewicz

2019

Appl Intell

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In this paper, we propose a classification system which is able to decide whether a company is innovative or not, based only on its public website available on the internet. As innovativeness plays a crucial role in the development of myriad branches of the modern economy, an increasing number of entities are expending effort to be innovative. Thus, a new issue has appeared: how can we recognise them? Not only is grasping the idea of innovativeness challenging for humans, but also impossible for any known machine learning algorithm. Therefore, we propose a new indirect technique: a diversified stacked generalisation method, which is based on a combination of a multi-view approach and a genetic algorithm. The proposed approach achieves better performance than all other classification methods which include: (i) models trained on single datasets; or (ii) a simple voting method on these models. Furthermore, in this study, we check if unaligned feature space improves classification results. The proposed solution has been extensively evaluated on real data collected from companies' websites. The experimental results verify that the proposed method improves the classification quality of websites which might represent innovative companies.

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Section: Related Work Vs Proposed Approachmentioning

confidence: 99%

Section: Innovative Website Recognition Systemmentioning

confidence: 99%

Section: Aligned Space Of Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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