4 research outputs found
Asymptotically Unbiased Estimation of A Nonsymmetric Dependence Measure Applied to Sensor Data Analytics and Financial Time Series
A fundamental concept frequently applied to statistical machine learning is the detection of dependencies between unknown random variables found from data samples. In previous work, we have introduced a nonparametric unilateral dependence measure based on Onicescuâs information energy and a kNN method for estimating this measure from an available sample set of discrete or continuous variables. This paper provides the formal proofs which show that the estimator is asymptotically unbiased and has asymptotic zero variance when the sample size increases. It implies that the estimator has good statistical qualities. We investigate the performance of the estimator for data analysis applications in sensor data analysis and financial time series
Inferring Feature Relevances From Metric Learning
Schulz A, Mokbel B, Biehl M, Hammer B. Inferring Feature Relevances From Metric Learning. In: 2015 IEEE Symposium Series on Computational Intelligence. Piscataway, NJ: IEEE; 2015
A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions
Traditional recommendation systems are faced with two long-standing
obstacles, namely, data sparsity and cold-start problems, which promote the
emergence and development of Cross-Domain Recommendation (CDR). The core idea
of CDR is to leverage information collected from other domains to alleviate the
two problems in one domain. Over the last decade, many efforts have been
engaged for cross-domain recommendation. Recently, with the development of deep
learning and neural networks, a large number of methods have emerged. However,
there is a limited number of systematic surveys on CDR, especially regarding
the latest proposed methods as well as the recommendation scenarios and
recommendation tasks they address. In this survey paper, we first proposed a
two-level taxonomy of cross-domain recommendation which classifies different
recommendation scenarios and recommendation tasks. We then introduce and
summarize existing cross-domain recommendation approaches under different
recommendation scenarios in a structured manner. We also organize datasets
commonly used. We conclude this survey by providing several potential research
directions about this field
TĂ€pne ja tĂ”hus protsessimudelite automaatne koostamine sĂŒndmuslogidest
Töötajate igapĂ€evatöö koosneb tegevustest, mille eesmĂ€rgiks on teenuste pakkumine vĂ”i toodete valmistamine. Selliste tegevuste terviklikku jada nimetatakse protsessiks. Protsessi kvaliteet ja efektiivsus mĂ”jutab otseselt kliendi kogemust â tema arvamust ja hinnangut teenusele vĂ”i tootele. Kliendi kogemus on eduka ettevĂ”tte arendamise oluline tegur, mis paneb ettevĂ”tteid jĂ€rjest rohkem pöörama tĂ€helepanu oma protsesside kirjeldamisele, analĂŒĂŒsimisele ja parendamisele.
Protsesside kirjeldamisel kasutatakse tavaliselt visuaalseid vahendeid, sellisel kujul koostatud kirjeldust nimetatakse protsessimudeliks. Kuna mudeli koostaja ei suuda panna kirja kÔike erandeid, mis vÔivad reaalses protsessis esineda, siis ei ole need mudelid paljudel juhtudel terviklikud. Samuti on probleemiks suur töömaht - inimese ajakulu protsessimudeli koostamisel on suur.
Protsessimudelite automaatne koostamine (protsessituvastus) vÔimaldab genereerida protsessimudeli toetudes tegevustega seotud andmetele. Protsessituvastus aitab meil vÀhendada protsessimudeli loomisele kuluvat aega ja samuti on tulemusena tekkiv mudel (vÔrreldes kÀsitsi tehtud mudeliga) kvaliteetsem. Protsessituvastuse tulemusel loodud mudeli kvaliteet sÔltub nii algandmete kvaliteedist kui ka protsessituvastuse algoritmist.
Antud doktoritöös anname ĂŒlevaate erinevatest protsessituvastuse algoritmidest. Toome vĂ€lja puudused ja pakume vĂ€lja uue algoritmi Split Miner. VĂ”rreldes olemasolevate algoritmidega on Splint Miner kiirem ja annab tulemuseks kvaliteetsema protsessimudeli. Samuti pakume vĂ€lja uue lĂ€henemise automaatselt koostatud protsessimudeli korrektsuse hindamiseks, mis on vĂ”rreldes olemasolevate meetoditega usaldusvÀÀrsem. Doktoritöö nĂ€itab, kuidas kasutada optimiseerimise algoritme protsessimudeli korrektsuse suurendamiseks.Everyday, companiesâ employees perform activities with the goal of providing services (or products) to their customers. A sequence of such activities is known as business process. The quality and the efficiency of a business process directly influence the customer experience. In a competitive business environment, achieving a great customer experience is fundamental to be a successful company. For this reason, companies are interested in identifying their business processes to analyse and improve them.
To analyse and improve a business process, it is generally useful to first write it down in the form of a graphical representation, namely a business process model. Drawing such process models manually is time-consuming because of the time it takes to collect detailed information about the execution of the process. Also, manually drawn process models are often incomplete because it is difficult to uncover every possible execution path in the process via manual data collection.
Automated process discovery allows business analysts to exploit process' execution data to automatically discover process models. Discovering high-quality process models is extremely important to reduce the time spent enhancing them and to avoid mistakes during process analysis. The quality of an automatically discovered process model depends on both the input data and the automated process discovery application that is used.
In this thesis, we provide an overview of the available algorithms to perform automated process discovery. We identify deficiencies in existing algorithms, and we propose a new algorithm, called Split Miner, which is faster and consistently discovers more accurate process models than existing algorithms. We also propose a new approach to measure the accuracy of automatically discovered process models in a fine-grained manner, and we use this new measurement approach to optimize the accuracy of automatically discovered process models.https://www.ester.ee/record=b530061