### 金融代写|金融计量经济学Financial Econometrics代考|Relevance of Information

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• (Generalized) Linear Models 广义线性模型
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## 金融代写|金融计量经济学Financial Econometrics代考|Relevance of Information

This aspect corresponds to the adequacy of the retrieved information provided to the user with his/her query, or more generally his/her needs or expectations. This issue has extensively been analyzed and many kinds of solutions have been proposed in a fuzzy setting, as described in Zadeh’s paper (2006), which pointed out the various aspects of relevance in the semantic web, in particular topic relevance, question relevance and the consideration of perception-based information. Fuzzy compatibility measures are investigated in Cross (1994) to evaluate the relevance in information retrieval.

Fuzzy formal concept analysis brings efficient solutions to the representation of information in order to retrieve relevant information (Medina et al. 2009; Lai and Zhang 2009; De Maio et al. 2012). Fuzzy ontologies are also used to improve relevance, as described in Calegari and Sanchez (2007), Akinribido et al. (2011) and their automatic generation is studied in Tho et al. (2006). Fuzzy or possibilistic description logic can also be used (Straccia 1998; Straccia 2006; Qi et al. 2007) to facilitate the identification of significant elements of information to answer a query and to avoid inconsistencies (Couchariere et al. 2008; Lesot et al. 2008). Fuzzy clustering was also used to take into account relevance in text categorization (Lee and Jiang 2014).

The pertinence of images retrieved to satisfy a user query has particularly attracted the attention of researchers. Similarity measures may be properly chosen to achieve

a satisfying relevance (Zhao et al. 2003; Omhover and Detyniecki 2004). Fuzzy graph models are presented in Krishnapuram et al. (2004) to enable a matching algorithm to compare the model of the image to the model of the query. Machine learning methods are often used to improve the relevance, for instance active learning requesting the participation of the user in Chowdhury et al. (2012) or semi-supervised fuzzy clustering performing a meaningful categorization that will help image retrieval to be more relevant (Grira et al. 2005). Concept learning is performed thanks to fuzzy clustering in order to take advantage of past experiences (Bhanu and Dong 2002). The concept of fuzzy relevance is also addressed (Yap and Wu 2003) and linguistic relevance is explored (Yager and Petry 2005) to take into account perceptual subjectivity and human-like approach of relevance.

All these methods are representative of attempts to improve the relevance of the information obtained by a retrieval system to satisfy the user’s needs, based on a fuzzy set-based representation.

## 金融代写|金融计量经济学Financial Econometrics代考|Trust or Veracity of Information

The trustworthiness of information is crucial for all domains where users look for information. A solution to take this factor of quality into account lies in the definition of a degree of confidence attached to a piece of information (Lesot and Revault d’Allonnes 2017) to evaluate the uncertainty it carries and the confidence the user can have in it. We focus here on fuzzy set-based or possibilistic approaches.

First of all, the sources of information have a clear influence on the user’s trust of information (Revault d’Allonnes 2014), because of their own reliability mainly based on their importance and their reputation. Their competence on the subject of the piece of information is another element involved in the trust of information, for instance the Financial Times is more renowned and expert in economics than the Daily Mirror. The relevance of the source with respect to the event is an additional component of the user’s trust in a piece of information, be the relevance geographical or relating to the topic of the event. For instance, a local website such as wildfiretoday.com may be more relevant to obtain precise and updated information on bushfires than wellknown international media such as $B B C$ News. Moreover, a subjective uncertainty expressed by the source, such as “We believe” or “it seems”, is an element of the trustworthiness of the source.

The content of the piece of information about an event also bears a part of uncertainty environ is inherent in the formulation itself through numerical imprecisions (“around 150 persons died” or “between 1000 and 1200 cases of infection”) or symbolic ones (“many participants”). Linguistic descriptions of uncertainty can also be present “”probably”, “almost certainly”, ” 69 homes believed destroyed”, “2 will probably survive”). Uncertain information can also be the consequence of an insufficient compatibility between several pieces of information on the same event. A fuzzy set-based knowledge representation contributes to taking into account imprecisions and to evaluating the compatibility between several descriptions such as «more than 70» and «approximately 75». The large range of aggregation methods in a fuzzy setting helps to achieve the fusion of pieces of information on a given event in order to confirm or invalidate each of them through a comparison with others, and to therefore overcome compatibility problems.

## 金融代写|金融计量经济学Financial Econometrics代考|Understandability of Information

The last component of the quality of information content we consider is its understandability or expressiveness. It is a complex notion (Marsala and Bouchon-Meunier 2015; Hüllermeier 2015), dealing with the understandability of the process leading to the presented information, as well as the easiness for the end user to interpret the piece of information he receives. This component has been widely investigated since the introduction of Explainable Artificial Intelligence (XAI) by DARPA in 2016 (https://www.darpa.mil/program/explainable-artificial-intelligence), that requires an explainable model and an explanation interface.

Fuzzy models are recognized for their capability to be understood. In particular, fuzzy rule-based systems are considered to be easily understandable because rules of the form “If the current return is Low or the current return is High, then low or high future returns are rather likely” (Van den Berg et al. 2004) contain symbolic descriptions similar to what specialists express. Fuzzy decision trees (Laurent et al. 2003; Bouchon-Meunier and Marsala 1999) are also very efficient models of the reasons why a conclusion is presented to the user. Nevertheless, a balance between complexity, accuracy and understandability of such fuzzy models is necessary (Casillas et al. 2003). The capacity of the user to understand the system represented by the fuzzy model depends not only on the semantic interpretability induced by natural-language like descriptions, but also on the number of attributes involved in premises and the number of rules (Gacto et al. 2011). The interpretability of a fuzzy model is a subjective appreciation and it is possible to distinguish highlevel criteria such as compactness, completeness, consistency and transparency of fuzzy rules, from low-level criteria such as coverage, normality or distinguishability of fuzzy modalities (Zhou and Gan 2008).

The interpretability of information presented to the user depends on the expertise of the user. Linguistic descriptions are not the only expected form of information extracted from time series or large databases, for instance. Formal logic, statistics or graphs may look appealing to experts. However, we focus here on fuzzy methods that provide linguistic information by means of fuzzy modalities such as “rapid increase” or “low cost” and fuzzy quantifiers like “a large majority” or “very few”. The wide range of works on linguistic summarization of big databases and time series by means of fuzzy descriptions and so-called protoforms (Zadeh 2002) shows the importance of the topic, starting from seminal definitions of linguistic summaries (Yager 1982; Kacprzyk and Yager 2001). Their interpretability can be questioned (Lesot et al. 2016) and improved by means of automatic methods, such as mathematical morphology (Moyse et al. 2013) or evolutionary computation (Altintop et al. 2017 ), among other methods.

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