1,721,042 research outputs found
Ensemble Classification with Random Projections: classifier selection and variable importance
No full textRandom Projections (RP) ensemble classifiers allow to improve classification accuracy while extending to the high-dimensional context methods originally developed for low dimensional data. However, reducing {em redundancy} and understanding the properties of the variable ranking induced by the RP ensemble classifier are still open issues. In fact, despite such classifiers highly improve the classification accuracy, they do not allow the identification of the variables with the highest discriminative power and their performance could still be enhanced by a suitable selection of a good subset of them.
With the aim to identify both the most accurate subset of classifiers and the most discriminant input features, in this work we investigated two different directions. On one hand, combining the original idea of using the Multiplicative Binomial Distribution (MBD) as the reference model to describe and predict the ensemble accuracy and an important result on such distribution, we devised a simple forward-selection technique called Ensemble Selection Algorithm (ESA).
On the other, inspired by the Random Forest (RF) process for feature selection, we adjusted the RP ensemble classifier so as to keep the information on variable importance. Specifically, we measured the relative importance of each input feature through a specific coefficient, called Variable Importance in Projection (VIP), and then we removed the variables that present the smallest values of such coefficient.
Results of applying both the ESA and the VIP criterion in simulated and real data demonstrate that our proposal successfully controls the misclassification rate by using a very small number of individual classifiers and by ranking the features in terms of their discriminative power
High-dimensional model-based clustering via random projections
No full textRandom projections (RPs) have shown to provide promising results in the context of high-dimensional supervised classification. In this work, we address the unsupervised classification issue by exploiting the general idea of RP ensemble. Specifically, we generate a set of low dimensional independent random projections and we perform a model-based clustering on each of them. The top B* projections, i.e. the projections which show the best grouping structure, are then retained. The final partition is obtained by aggregating the chosen classifiers via consensus. The performances of the method are assessed on a set of both real and simulated data
High-dimensional Clustering with Random Projections
No full textRandom projections (RPs) have shown to provide promising results for high-dimensional classification. In this work, we address the issue of high-dimensional clustering by exploiting the general idea of RP ensemble to perform unsupervised classification. Specifically, we generate a set of low dimensional independent random projections and we perform a model-based clustering on each of them. The top B1 projections, i.e. the ones showing the best grouping structure according to different cluster quality measures, are then selected. The final partition is obtained by aggregating, via consensus, the chosen classifiers. The performances of the method are assessed on a set of both real and simulated data
La scelta di gestione in-house dei servizi pubblici locali nella prospettiva comunitaria
No full text1. L’art. 106 TFUE: le deroghe alla concorrenza ammesse dall'ordinamento comunitario. — 2. La disciplina comunitaria sui SIEG. — 3. La disciplina comunitaria
sugli Aiuti di Stato e sugli appalti pubblici. — 4. La trasparenza delle relazioni finanziarie tra poteri pubblici e imprese pubbliche. — 5. Le previsioni sulle società
in-house nello schema di decreto legislativo contenente il TUSPL. — 6. Conclusioni. — Post Scriptum
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
- …
