8,967 research outputs found
Enhanced default risk models with SVM+
Default risk models have lately raised a great interest due to the recent world economic crisis. In spite of many advanced techniques that have extensively been proposed, no comprehensive method incorporating a holistic perspective has hitherto been considered. Thus, the existing models for bankruptcy prediction lack the whole coverage of contextual knowledge which may prevent the decision makers such as investors and financial analysts to take the right decisions. Recently, SVM+ provides a formal way to incorporate additional information (not only training data) onto the learning models improving generalization. In financial settings examples of such non-financial (though relevant) information are marketing reports, competitors landscape, economic environment, customers screening, industry trends, etc. By exploiting additional information able to improve classical inductive learning we propose a prediction model where data is naturally separated into several structured groups clustered by the size and annual turnover of the firms. Experimental results in the setting of a heterogeneous data set of French companies demonstrated that the proposed default risk model showed better predictability performance than the baseline SVM and multi-task learning with SVM.info:eu-repo/semantics/publishedVersio
Deep Generative Models for Reject Inference in Credit Scoring
Credit scoring models based on accepted applications may be biased and their
consequences can have a statistical and economic impact. Reject inference is
the process of attempting to infer the creditworthiness status of the rejected
applications. In this research, we use deep generative models to develop two
new semi-supervised Bayesian models for reject inference in credit scoring, in
which we model the data generating process to be dependent on a Gaussian
mixture. The goal is to improve the classification accuracy in credit scoring
models by adding reject applications. Our proposed models infer the unknown
creditworthiness of the rejected applications by exact enumeration of the two
possible outcomes of the loan (default or non-default). The efficient
stochastic gradient optimization technique used in deep generative models makes
our models suitable for large data sets. Finally, the experiments in this
research show that our proposed models perform better than classical and
alternative machine learning models for reject inference in credit scoring
Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants
Within the field of soft computing, intelligent optimization modelling techniques include
various major techniques in artificial intelligence. These techniques pretend to generate new business
knowledge transforming sets of "raw data" into business value. One of the principal applications of
these techniques is related to the design of predictive analytics for the improvement of advanced
CBM (condition-based maintenance) strategies and energy production forecasting. These advanced
techniques can be used to transform control system data, operational data and maintenance event data
to failure diagnostic and prognostic knowledge and, ultimately, to derive expected energy generation.
One of the systems where these techniques can be applied with massive potential impact are the
legacy monitoring systems existing in solar PV energy generation plants. These systems produce a
great amount of data over time, while at the same time they demand an important e ort in order to
increase their performance through the use of more accurate predictive analytics to reduce production
losses having a direct impact on ROI. How to choose the most suitable techniques to apply is one of
the problems to address. This paper presents a review and a comparative analysis of six intelligent
optimization modelling techniques, which have been applied on a PV plant case study, using the
energy production forecast as the decision variable. The methodology proposed not only pretends
to elicit the most accurate solution but also validates the results, in comparison with the di erent
outputs for the di erent techniques
Application of support vector machines on the basis of the first Hungarian bankruptcy model
In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks
Social Bots for Online Public Health Interventions
According to the Center for Disease Control and Prevention, in the United
States hundreds of thousands initiate smoking each year, and millions live with
smoking-related dis- eases. Many tobacco users discuss their habits and
preferences on social media. This work conceptualizes a framework for targeted
health interventions to inform tobacco users about the consequences of tobacco
use. We designed a Twitter bot named Notobot (short for No-Tobacco Bot) that
leverages machine learning to identify users posting pro-tobacco tweets and
select individualized interventions to address their interest in tobacco use.
We searched the Twitter feed for tobacco-related keywords and phrases, and
trained a convolutional neural network using over 4,000 tweets dichotomously
manually labeled as either pro- tobacco or not pro-tobacco. This model achieves
a 90% recall rate on the training set and 74% on test data. Users posting pro-
tobacco tweets are matched with former smokers with similar interests who
posted anti-tobacco tweets. Algorithmic matching, based on the power of peer
influence, allows for the systematic delivery of personalized interventions
based on real anti-tobacco tweets from former smokers. Experimental evaluation
suggests that our system would perform well if deployed. This research offers
opportunities for public health researchers to increase health awareness at
scale. Future work entails deploying the fully operational Notobot system in a
controlled experiment within a public health campaign
Mapping Subsets of Scholarly Information
We illustrate the use of machine learning techniques to analyze, structure,
maintain, and evolve a large online corpus of academic literature. An emerging
field of research can be identified as part of an existing corpus, permitting
the implementation of a more coherent community structure for its
practitioners.Comment: 10 pages, 4 figures, presented at Arthur M. Sackler Colloquium on
"Mapping Knowledge Domains", 9--11 May 2003, Beckman Center, Irvine, CA,
proceedings to appear in PNA
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