1,721,040 research outputs found
Detecting and interpreting the consensus ranking based on the weighted Kemeny distance
No full textThis paper outlines a way for finding the consensus ranking minimizing the sum of the weighted Kemeny distance, using positional weights. The weighted Kemeny distance, introduced by Garc ́ıa-Lapresta and Perez-Rom ́ an, meets the original ́ Kemeny-Snell axioms and it is fully applicable in treating weak orderings. A differential evolution algorithm is ad-hoc defined in order to detect the consensus ranking, namely that ranking that best represents the preferences expressed by a set of individuals
Dynamic modelling of price expectations
No full textEvaluation surveys are often repeated over time in order to check for
trends in subjects’ behaviors and opinions. The paper proposes a dynamic model for
the serial correlation of ratings’ intrinsic components, which is discussed on the basis
of time series of price expectations in Italy collected within a survey organized by
ISTAT
MACHINE LEARNING MODELS FOR FORECASTING STOCK TRENDS
No full textThis research addresses the problem of predicting the trends of two
stocks and two stock indexes for the American stock market. In this study, the predictive performance of four machine learning models, are compared. The models investigated include Artificial Neural Networks (ANN), Support Vector Machine (SVM),
Random Forest and Naive-Bayes. Supervised models training is performed through a
10-fold CV approach repeated 3 times, using 10 of the main indicators and oscillators
of technical analysis as input. The experiments conducted show that among the 4,
the Naive-Bayes model gives the worst predictive performance, the Random Forest
obtains discrete results, while the SVM and the ANN are the best performing models
INFLUENCE OF OUTLIERS ON CLUSTER CORRESPONDENCE ANALYSIS
No full textThis paper focuses on determining the influence of outliers on a joint dimension reduction and clustering method for categorical data, namely Cluster Cor- respondence Analysis (CCA). Joint methods, such as CCA, solutions consist of both a cluster membership vector and a set of low dimensional scores for observations and attributes. We evaluate the impact of outliers on the identification of the cluster struc- ture. As a benchmark, we use the tandem approach, which is a sequential application of multiple correspondence analysis followed by K-means clustering. The appraisal is based on synthetic data and outliers generated using an evolutionary algorithm that provides data with a user-defined cluster structure
Detecting Wine Adulterations Employing Robust Mixture of Factor Analyzers
Get PDFAn authentic food is one that is what it claims to be. Nowadays, more and more attention is devoted to the food market: stakeholders, throughout the value chain, need to receive exact information about the specific product they are commercing with. To ascertain varietal genuineness and distinguish potentially doctored food, in this paper we propose to employ a robust mixture estimation method. Particularly, in a wine authenticity framework with unobserved heterogeneity, we jointly perform genuine wine classification and contamination detection. Our methodology models the data as arising from a mixture of Gaussian factors and depicts the observations with the lowest contributions to the overall likelihood as illegal samples. The advantage of using robust estimation on a real wine dataset is shown, in comparison with many other classification approaches. Moreover, the simulation results confirm the effectiveness of our approach in dealing with an adulterated dataset
Tree embedded linear mixed models
No full textThis work gives a contribution to the emerging literature on the use of regression trees for hierarchical data to increase the flexibility and the predictive ability of random effects models. The proposed procedure extends random effect re- gression trees considering a random effect model with both a tree component and a linear component. Moreover, it is suggested to decompose the effects of predictors within and between clusters. The performance of the proposed procedure is evaluated through a simulation study and an application to INVALSI data on students achieve- ment
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