Institut du management de l'information
A.L. Breguet 2
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- PublicationAccès libre
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- PublicationAccès libreCrime Linkage: a Fuzzy MCDM Approach(: IEEE, 2013-6-4)
; ; ;Grossrieder, Lionel ;Ribaux, OlivierGrouping crimes having similarities has always been interesting for analysts. Actually, when a set of crimes share common properties, the capability to conduct reasoning and the automation with this set drastically increase. Conjunction, interpretation and explanation based on similarities can be key success factors to apprehend criminals. In this paper, we present a computerized method for high-volume crime linkage, based on a fuzzy MCDM approach in order to combine situational, behavioral, and forensic information. Experiments are conducted with series in burglaries from real data and compared to expert results.
- PublicationMétadonnées seulementSelecting optimal split-functions for large datasets(Godalming: Springer-Verlag London Ltd, 2001)
; ;Raileanu, Laura Elena ;Bramer, Max ;Preece, AlunCoenen, FransDecision tree induction has become one of the most popular methods for classification and prediction. The key step in the process of inferring decision trees is finding the right criteria for splitting the training set into smaller and smaller subsets so that, ideally, all elements of a subset finally belong to one class. These split criteria can be defined in different ways (e.g. minimizing impurity of a subset, or minimizing entropy in a subset), and therefore they emphasize different properties of the inferred tree, such as size or classification accuracy. In this paper we analyze if the split functions introduced in a statistical and machine learning context are also well suited for a KDD context. We selected two well known split functions, namely Gini Index (CART) and Information Gain (C4.5) and introduced our own family of split functions and tested them on 9,000 data sets of different sizes (from 200 to 20, 000 tuples). The tests have shown that the two popular functions are very sensitive to the variation of the training set sizes and therefore the quality of the inferred trees is highly dependent on the training set size. At the same time however, we were able to show that the simplest members of the introduced family of split functions behave in a very predictable way and, furthermore, the created trees were superior to the trees inferred using the Gini Index or the Information Gain based on our evaluation criteria.
- PublicationAccès libreWatch-dictaphone for Automatic Medical Codification(2008)
;Grassi, Sara ;Heck, P. ;Stadelmann, Patrick ; ;Dinissen, P. ;Meylan, F. ;Mignot, J.-P. ;Pfeuti, J.-N. ;Piccini, F. ;Geiser, P. ;Biundo, Giuseppina ;Farine, Pierre-André ;Grupp, JoachimThis paper describes the development of a voice data recording watch and the accompanying software and hardware needed to build an integrated solution for medical data capture and auto-matic codification. The developed system is used to capture on the stand-alone watch, either by voice recording or by form-filling, the relevant information on the healthcare service being provided. The captured information is stored locally and then downloaded to a PC, via a USB connection. In the PC, speech is translated first into text and then into medical codification, with minimum user-intervention. The resulting information is stored into local or cen-tralized patient databases, to be sent later to 3rd-party SW for billing, reporting and statistics. The user controls the watch either via a touch-screen based interface or via a voice-driven interface for hands-free operation. Although this work is multidisciplinary, in this paper the emphasis is put on the description of the DSP implementation.
- PublicationMétadonnées seulementQuantification de la musculature lisse intrapénienne avec un algorithme de logique floue= Quantification of smooth muscle tissue using fuzzy logic(: Elsevier, 1993)
;Kasas, Sandor ;Wisard, Marc
- PublicationAccès libreSwiss Health Metadata Repository: prototype evaluation(Université de Neuchâtel Institut du management de l'information, 2021-6-30)
- PublicationAccès libreFCA-Based Ontology Learning From Unstructured Textual Data(: Springer, 2018-12-20)Ontologies have been frequently used for representing a domain knowledge. It has a lot of applications in semantic knowledge extraction. However, learning ontologies especially from unstructured data is a difficult yet an interesting challenge. In this paper, we introduce a pipeline for learning ontology from a text corpora in a semi-automated fashion using Natural Language Processing (NLP) and Formal Concept Analysis (FCA). We apply our proposed method on a small given corpus that consists of some news documents in IT and pharmaceutical domain. We then discuss the potential applications of the proposed model and ideas on how to improve it even further.
- PublicationMétadonnées seulementAn Ontology to Support Non-Invasive Diagnosis of Heritage Metals(2016)
; ;Vonlanthen, Yves ;Rosselet, Antoine ;Degrigny, ChristianGaspoz, Cédric