20 research outputs found
Automatic and Accurate Classification of Hotel Bathrooms from Images with Deep Learning
Hotel bathrooms are one of the most important places in terms of customer
satisfaction, and where the most complaints are reported. To share their
experiences, guests rate hotels, comment, and share images of their positive or
negative ratings. An important part of the room images shared by guests is
related to bathrooms. Guests tend to prove their satisfaction or
dissatisfaction with the bathrooms with images in their comments. These
Positive or negative comments and visuals potentially affect the prospective
guests. In this study, two different versions of a deep learning algorithm were
designed to classify hotel bathrooms as satisfactory (good) or unsatisfactory
(bad, when any defects such as dirtiness, deficiencies, malfunctions were
present) by analyzing images. The best-performer between the two models was
determined as a result of a series of extensive experimental studies. The
models were trained for each of 144 combinations of 5 hyper-parameter sets with
a data set containing more than 11 thousand bathroom images, specially created
for this study. The "HotelBath" data set was shared also with the community
with this study. Four different image sizes were taken into consideration: 128,
256, 512 and 1024 pixels in both directions. The classification performances of
the models were measured with several metrics. Both algorithms showed very
attractive performances even with many combinations of hyper-parameters. They
can classify bathroom images with very high accuracy. Suh that the top
algorithm achieved an accuracy of 92.4% and an AUC (area under the curve) score
of 0.967. In addition, other metrics also proved the success..
Unwrapping black box models: a case study in credit risk
The past two decades have witnessed the rapid development of machine learning
techniques, which have proven to be powerful tools for the construction of predictive
models, such as those used in credit risk management. A considerable volume of
published work has looked at the utility of machine learning for this purpose, the
increased predictive capacities delivered and how new types of data can be
exploited. However, these benefits come at the cost of increased complexity, which
may render the models uninterpretable. To overcome this issue a new field has
emerged under the name of explainable artificial intelligence, with numerous tools
being proposed to gain an insight into the inner workings of these models. This type
of understanding is fundamental in credit risk in order to ensure compliance with the
existing regulatory requirements and to comprehend the factors driving the
predictions and their macro-economic implications. This paper studies the
effectiveness of some of the most widely-used interpretability techniques on a neural
network trained on real data. These techniques are found to be useful for
understanding the model, even though some limitations have been encountered.En las dos últimas décadas se ha observado un rápido desarrollo de las técnicas
de aprendizaje automático, que han demostrado ser herramientas muy potentes
para elaborar modelos de predicción, como los utilizados en la gestión del riesgo de
crédito. En un volumen considerable de trabajos publicados se analizan la utilidad del
aprendizaje automático para este fin, las mayores capacidades predictivas que
ofrece y la forma en la que se pueden explotar nuevos tipos de datos. Sin embargo,
estas ventajas llevan aparejada una mayor complejidad, que puede imposibilitar la
interpretación de los modelos. Para solventar este punto ha surgido un nuevo campo
de investigación, denominado «inteligencia artificial explicable» (del inglés explicable
artificial intelligence), en el que se proponen numerosas herramientas para obtener
información relativa al funcionamiento interno de estos modelos. Este tipo de
conocimiento es fundamental en materia de riesgo de crédito para garantizar que se
cumplen los requerimientos regulatorios existentes y para comprender los factores
determinantes de las predicciones y sus implicaciones macroeconómicas. En este
artÃculo se estudia la eficacia de algunas de las técnicas de interpretabilidad más
utilizadas en una red neuronal entrenada con datos reales. Estas técnicas se
consideran útiles para la comprensión del modelo, pese a que se han detectado
algunas limitaciones
Explainable AI for Interpretable Credit Scoring
With the ever-growing achievements in Artificial Intelligence (AI) and the
recent boosted enthusiasm in Financial Technology (FinTech), applications such
as credit scoring have gained substantial academic interest. Credit scoring
helps financial experts make better decisions regarding whether or not to
accept a loan application, such that loans with a high probability of default
are not accepted. Apart from the noisy and highly imbalanced data challenges
faced by such credit scoring models, recent regulations such as the `right to
explanation' introduced by the General Data Protection Regulation (GDPR) and
the Equal Credit Opportunity Act (ECOA) have added the need for model
interpretability to ensure that algorithmic decisions are understandable and
coherent. An interesting concept that has been recently introduced is
eXplainable AI (XAI), which focuses on making black-box models more
interpretable. In this work, we present a credit scoring model that is both
accurate and interpretable. For classification, state-of-the-art performance on
the Home Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is
achieved using the Extreme Gradient Boosting (XGBoost) model. The model is then
further enhanced with a 360-degree explanation framework, which provides
different explanations (i.e. global, local feature-based and local
instance-based) that are required by different people in different situations.
Evaluation through the use of functionallygrounded, application-grounded and
human-grounded analysis show that the explanations provided are simple,
consistent as well as satisfy the six predetermined hypotheses testing for
correctness, effectiveness, easy understanding, detail sufficiency and
trustworthiness.Comment: 19 pages, David C. Wyld et al. (Eds): ACITY, DPPR, VLSI, WeST, DSA,
CNDC, IoTE, AIAA, NLPTA - 202