Double clustering for rating mutual funds

Due to the increasing proliferation of mutual funds, in-depth evaluationof the available products for portfolio selection purposes is a difficult task.Hence, classification schemes giving quick information about which fundsare worth to be monitored, are often provided. The aim of this work is toshow an application of clustering methods to the mutual funds historicaldata. Starting from the monthly time series of the Net Asset Values of aspecific style-based category, namely the Large Blend US mutual funds, weapply distance-based clustering methods twice on a set of return, risk andperformance measures: firstly, with the aim of reducing data dimension, andsecondly to cluster funds in homogeneous classes. The adopted procedureclaims the feature of producing a partition of funds that are readily inter-pretable from a financial point of view and it is further possible to rank theidentified groups, thus obtaining a rating of funds that turns out to accountfor different propensities toward the risk exposure

One-Sided Prototype Selection on Class Imbalanced Dissimilarity Matrices

In the dissimilarity representation paradigm, several prototype selection methods have been used to cope with the topic of how to select a small representation set for generating a low-dimensional dissimilarity space. In addition, these methods have also been used to reduce the size of the dissimilarity matrix. However, these approaches assume a relatively balanced class distribution, which is grossly violated in many real-life problems. Often, the ratios of prior probabilities between classes are extremely skewed. In this paper, we study the use of renowned prototype selection methods adapted to the case of learning from an imbalanced dissimilarity matrix. More specifically, we propose the use of these methods to under-sample the majority class in the dissimilarity space. The experimental results demonstrate that the one-sided selection strategy performs better than the classical prototype selection methods applied over all classes

Poverty Alleviation and the Degree of Centralisation in European Schemes of Social Assistance

Social Assistance; Classification; Centralisation; Poverty; Redistribution

Machine learning and applications in microbiology

To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution