Source behavior discovery for fusion of subjective opinions

Name: Source behavior discovery for fusion of subjective opinions
Author: Şensoy, Murat, Kaplan, L., Mel, G. de, Gunes, Taha

İsim	Source behavior discovery for fusion of subjective opinions
Yazar	Şensoy, Murat, Kaplan, L., Mel, G. de, Gunes, Taha
Basım Tarihi:	2016
Basım Yeri	- IEEE
Konu	Reliability, Training, Estimation, Transforms, Decision making, Data models, Correlation
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-0-9964-5274-8
Kayıt Numarası	e8f65a04-2f1a-4302-a32d-6fb1c5b35247
Lokasyon	Computer Science
Tarih	2016
Örnek Metin	Information is at the center of decision making in many systems and use-cases. In cooperative or hostile environments, agents communicate their subjective opinions about various phenomenon. However, sources of these opinions are not always competent and honest but often erroneous or even malicious. Furthermore, malicious sources may adopt certain behaviors to mislead the decision maker in a specific way. Fortunately, the reports of such misleading sources are correlated to ground truth. Using statistical methods, one can learn how likely a source distorts the ground truth and the associated distortion models so that reports from these sources can still be fused to enhance estimation of the ground truth. In this work, we propose to learn a number of statistically meaningful opinion transformations that represent various behaviors of information sources. Then, we exploit these transformations while fusing opinions from unreliable sources. Using real data from the Web and through extensive comparison with recent trust-based approaches over simulations, we show that our approach can be used to determine set of transformations that may lead to more accurate estimation of the truth.

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Source behavior discovery for fusion of subjective opinions

Yazar Şensoy, Murat, Kaplan, L., Mel, G. de, Gunes, Taha

Basım Tarihi 2016

Basım Yeri - IEEE

Konu Reliability, Training, Estimation, Transforms, Decision making, Data models, Correlation

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-0-9964-5274-8

Kayıt Numarası e8f65a04-2f1a-4302-a32d-6fb1c5b35247

Lokasyon Computer Science

Tarih 2016

Örnek Metin Information is at the center of decision making in many systems and use-cases. In cooperative or hostile environments, agents communicate their subjective opinions about various phenomenon. However, sources of these opinions are not always competent and honest but often erroneous or even malicious. Furthermore, malicious sources may adopt certain behaviors to mislead the decision maker in a specific way. Fortunately, the reports of such misleading sources are correlated to ground truth. Using statistical methods, one can learn how likely a source distorts the ground truth and the associated distortion models so that reports from these sources can still be fused to enhance estimation of the ground truth. In this work, we propose to learn a number of statistically meaningful opinion transformations that represent various behaviors of information sources. Then, we exploit these transformations while fusing opinions from unreliable sources. Using real data from the Web and through extensive comparison with recent trust-based approaches over simulations, we show that our approach can be used to determine set of transformations that may lead to more accurate estimation of the truth.