Cost-sensitive churn prediction in fund management services

© Springer International Publishing AG, part of Springer Nature 2018. Churn prediction is vital to companies as to identify potential churners and prevent losses in advance. Although it has been addressed as a classification task and a variety of models have been employed in practice, fund management services have presented several special challenges. One is that financial data is extremely imbalanced since only a tiny proportion of customers leave every year. Another is a unique cost-sensitive learning problem, i.e., costs of wrong predictions for churners should be related to their account balances, while costs of wrong predictions for non-churners should be the same. To address these issues, this paper proposes a new churn prediction model based on ensemble learning. In our model, multiple classifiers are built using sampled datasets to tackle the imbalanced data issue while exploiting data fully. Moreover, a novel sampling strategy is proposed to deal with the unique cost-sensitive issue. This model has been deployed in one of the leading fund management institutions in Australia, and its effectiveness has been fully validated in real applications

A first attempt on global evolutionary undersampling for imbalanced big data

The design of efficient big data learning models has become a common need in a great number of applications. The massive amounts of available data may hinder the use of traditional data mining techniques, especially when evolutionary algorithms are involved as a key step. Existing solutions typically follow a divide-and-conquer approach in which the data is split into several chunks that are addressed individually. Next, the partial knowledge acquired from every slice of data is aggregated in multiple ways to solve the entire problem. However, these approaches are missing a global view of the data as a whole, which may result in less accurate models. In this work we carry out a first attempt on the design of a global evolutionary undersampling model for imbalanced classification problems. These are characterised by having a highly skewed distribution of classes in which evolutionary models are being used to balance it by selecting only the most relevant data. Using Apache Spark as big data technology, we have introduced a number of variations to the well-known CHC algorithm to work very large chromosomes and reduce the costs associated to fitness evaluation. We discuss some preliminary results, showing the great potential of this new kind of evolutionary big data model

Assessment of Oak Groves Conservation Statuses in Natura 2000 Sacs with Single Photon Lidar and Sentinel-2 Data

Among the main objectives of Natura 2000 Network sites management plans is monitoring their conservation status under a reasonable cost and with high temporal frequency. The aim of this study is to assess the ability of single-photon light detection and ranging (LiDAR) technology (14 points per m2) and Sentinel-2 data to classify the conservation status of oak forests in four special areas of conservation in Navarra Province (Spain) that comprise three habitats. To capture the variability of conservation status within the three habitats, we first performed a random stratified sampling based on conservation status measured in the field, canopy cover, and terrain slope and height. Thereafter, we compared two metric selection approaches, namely Kruskal–Wallis and Dunn tests, and two machine learning classification methods, random forest (RF) and support vector machine (SVM), to classify the conservation statuses using LiDAR and Sentinel-2 data. The best-fit classification model, which included only LiDAR metrics, was obtained using the random forest method, with an overall classification accuracy after validation of 83.01%, 75.51%, and 88.25% for Quercus robur (9160), Quercus pyrenaica (9230), and Quercus faginea (9240) habitats, respectively. The models include three to six LiDAR metrics, with the structural diversity indices (LiDAR height evenness index, LHEI, and LiDAR height diversity index, LHDI) and canopy cover (FCC) being the most relevant ones. The inclusion of the NDVI index from the Sentinel-2 image did not improve the classification accuracy significantly. This approach demonstrates its value for classifying and subsequently mapping conservation statuses in oak groves and other Natura 2000 Network habitat sites at a regional scale, which could serve for more effective monitoring and management of high biodiversity habitats

Fuzzy entropy from weak fuzzy subsethood measures

In this paper, we propose a new construction method for fuzzy and weak fuzzy subsethood measures based on the aggregation of implication operators. We study the desired properties of the implication operators in order to construct these measures. We also show the relationship between fuzzy entropy and weak fuzzy subsethood measures constructed by our method

Acneiform lesions secondary to ZD1839, an inhibitor of the epidermal growth factor receptor

Drugs that inhibit the epidermal growth factor receptor, such as ZD1839 or C225, are being used increasingly in the treatment of solid tumours. This has led to the appearance of new secondary effects. We describe the case of a patient who presented with an acneiform eruption secondary to the administration of ZD1839. These lesions healed in a few days after stopping the dru

Systemic lupus erythematosus-associated anetoderma and anti-phospholipid antibodies

Anetoderma is characterized by a loss of normal elastic tissue that presents clinically as localized areas of wrinkled or flaccid skin. We describe the case of a 30-year-old woman with systemic lupus erythematosus-associated anetoderma and positive anti-phospholipid antibodies. We discuss the possible role of these antibodies in the pathogenesis of anetoderma, and, when detected, the need to check for an associated anti-phospholipid syndrome in such patients

Evolutionary undersampling for extremely imbalanced big data classification under apache spark

The classification of datasets with a skewed class distribution is an important problem in data mining. Evolutionary undersampling of the majority class has proved to be a successful approach to tackle this issue. Such a challenging task may become even more difficult when the number of the majority class examples is very big. In this scenario, the use of the evolutionary model becomes unpractical due to the memory and time constrictions. Divide-and-conquer approaches based on the MapReduce paradigm have already been proposed to handle this type of problems by dividing data into multiple subsets. However, in extremely imbalanced cases, these models may suffer from a lack of density from the minority class in the subsets considered. Aiming at addressing this problem, in this contribution we provide a new big data scheme based on the new emerging technology Apache Spark to tackle highly imbalanced datasets. We take advantage of its in-memory operations to diminish the effect of the small sample size. The key point of this proposal lies in the independent management of majority and minority class examples, allowing us to keep a higher number of minority class examples in each subset. In our experiments, we analyze the proposed model with several data sets with up to 17 million instances. The results show the goodness of this evolutionary undersampling model for extremely imbalanced big data classification

The role of nitric oxide synthases in pemphigus vulgaris in a mouse model

Pemphigus vulgaris (PV) is a blistering autoimmune disease characterized by IgG autoantibodies against desmoglein 3. Nitric oxide synthases (NOS) may contribute to the increase of inflammation in tissues by the generation of nitrotyrosine residues (NTR). OBJECTIVES: To investigate whether the production of NTR mediated by NOS may participate in the development of inflammation and acantholysis in PV. METHODS: Mice were pretreated or not with NOS, tyrosine-kinase (TK) or nuclear factor (NF)-kappaB inhibitors, and then injected with PV-IgG. PV manifestations were examined in all mice. The expression of NTR, constitutive NOS (cNOS) [endothelial NOS (eNOS) and neuronal NOS (nNOS)], inducible NOS (iNOS) and NF-kappaB factor were studied in epidermis of mice using immunohistochemical techniques. RESULTS: After PV-IgG injection, expressions of NTR, iNOS, eNOS and nNOS increased in acantholytic cells, as did nuclear translocation of NF-kappaB in the basal cells of the epidermis. Pretreatment of mice with inhibitors of TK, nNOS and nonselective NOS, completely prevented NTR expression and the clinical and histological findings of PV in mice. TK inhibitor genistein inhibited both nNOS and iNOS expression on the membrane of basal keratinocytes, and nuclear translocation of NF-kappaB. CONCLUSIONS: Upregulation of cNOS and iNOS, NTR generation and nuclear translocation of NF-kappaB may contribute to increased inflammation and tissue damage in PV lesions. The absence of the clinical and histological findings of PV and NTR expression in mice injected with PV-IgG, through pretreatment with TK and nNOS inhibitors, provides compelling evidence that these signalling molecules should be considered as potential therapeutic targets in PV

Therapeutic Drug Monitoring of Antifungal Drugs: Another Tool to Improve Patient Outcome?

Introduction: This study aimed to examine the relationship among adequate dose, serum concentration and clinical outcome in a non-selected group of hospitalized patients receiving antifungals. Methods: Prospective cross-sectional study performed between March 2015 and June 2015. Dosage of antifungals was considered adequate according to the IDSA guidelines, whereas trough serum concentrations (determined with HPLC) were considered adequate as follows: fluconazole > 11\ua0\ub5g/ml, echinocandins > 1\ua0\ub5g/ml, voriconazole 1\u20135.5\ua0\ub5g/ml and posaconazole > 0.7\ua0\ub5g/ml. Results: During the study period, 84 patients (65.4% male, 59.6\ua0years) received antifungals for prophylaxis (40.4%), targeted (31.0%) and empirical therapy (28.6%). The most frequent drug was micafungin (28/84; 33.3%) followed by fluconazole (23/84; 27.4%), voriconazole (15/84; 17.9%), anidulafungin (8/84; 9.5%), posaconazole (7/84; 8.3%) and caspofungin (3/84; 3.6%). Considerable interindividual variability was observed for all antifungals with a large proportion of the patients (64.3%) not attaining adequate trough serum concentrations, despite receiving an adequate antifungal dose. Attaining the on-target serum antifungal level was significantly associated with a favorable clinical outcome (OR = 0.02; 95% CI 0.01\u20130.64; p = 0.03), whereas the administration of an adequate antifungal dosage was not. Conclusions: With the standard antifungal dosage, a considerable proportion of patients have low drug concentrations, which are associated with poor clinical outcome