10,451 research outputs found
Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
As machine learning is increasingly used to make real-world decisions, recent
research efforts aim to define and ensure fairness in algorithmic decision
making. Existing methods often assume a fixed set of observable features to
define individuals, but lack a discussion of certain features not being
observed at test time. In this paper, we study fairness of naive Bayes
classifiers, which allow partial observations. In particular, we introduce the
notion of a discrimination pattern, which refers to an individual receiving
different classifications depending on whether some sensitive attributes were
observed. Then a model is considered fair if it has no such pattern. We propose
an algorithm to discover and mine for discrimination patterns in a naive Bayes
classifier, and show how to learn maximum likelihood parameters subject to
these fairness constraints. Our approach iteratively discovers and eliminates
discrimination patterns until a fair model is learned. An empirical evaluation
on three real-world datasets demonstrates that we can remove exponentially many
discrimination patterns by only adding a small fraction of them as constraints
Identifying Real Estate Opportunities using Machine Learning
The real estate market is exposed to many fluctuations in prices because of
existing correlations with many variables, some of which cannot be controlled
or might even be unknown. Housing prices can increase rapidly (or in some
cases, also drop very fast), yet the numerous listings available online where
houses are sold or rented are not likely to be updated that often. In some
cases, individuals interested in selling a house (or apartment) might include
it in some online listing, and forget about updating the price. In other cases,
some individuals might be interested in deliberately setting a price below the
market price in order to sell the home faster, for various reasons. In this
paper, we aim at developing a machine learning application that identifies
opportunities in the real estate market in real time, i.e., houses that are
listed with a price substantially below the market price. This program can be
useful for investors interested in the housing market. We have focused in a use
case considering real estate assets located in the Salamanca district in Madrid
(Spain) and listed in the most relevant Spanish online site for home sales and
rentals. The application is formally implemented as a regression problem that
tries to estimate the market price of a house given features retrieved from
public online listings. For building this application, we have performed a
feature engineering stage in order to discover relevant features that allows
for attaining a high predictive performance. Several machine learning
algorithms have been tested, including regression trees, k-nearest neighbors,
support vector machines and neural networks, identifying advantages and
handicaps of each of them.Comment: 24 pages, 13 figures, 5 table
The performance of auction houses selling Picasso Prints.
It has been observed that similar prints can obtain quite different prices at different auctions within the same auction period. Previous works applying hedonic price technique to determine the formation of auction prices of objects of art have found no conclusive result about the impact of auction houses on final prices. In these studies the object of art has been the unit, and influence of auction houses is analysed by testing whether auction house impact on price is significant or not within a framework of central tendencies. In order to focus on auction houses as a unit we have applied a benchmarking technique, DEA, developed for efficiency studies. Performance indexes are defined and calculated giving an insight into auction house differences difficult to obtain using hedonic price approach.Performance; auction house; Picasso prints; hedonic price; benchmarking; best practice; DEA
Biproportional Techniques in Input-Output Analysis: Table Updating and Structural Analysis
This paper is dedicated to the contributions of Sir Richard Stone, Michael Bacharach, and Philip Israilevich. It starts out with a brief history of biproportional techniques and related matrix balancing algorithms. We then discuss the RAS algorithm developed by Sir Richard Stone and others. We follow that by evaluating the interpretability of the product of the adjustment parameters, generally known as R and S. We then move on to discuss the various formal formulations of other biproportional approaches and discuss what defines an algorithm as ĂąâŹĆbiproportionalĂąâŹ. After mentioning a number of competing optimization algorithms that cannot fall under the rubric of being biproportional, we reflect upon how some of their features have been included into the biproportional setting (the ability to fix the value of interior cells of the matrix being adjusted and of incorporating data reliability into the algorithm). We wind up the paper by pointing out some areas that could use further investigation.Input-Output Economics; RAS; data raking; iterative proportional fitting; estimating missing data
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