1,365 research outputs found
Fairness and Interpretability in Machine Learning Models
Machine Learning has become more and more prominent in our daily lives as the Information Age and Fourth industrial revolution progresses. Many of these machine learning systems are evaluated in terms of how accurately they are able to predict the correct outcome that are present in existing historical datasets. In the last years we have observed how evaluating machine learning systems in this way has allowed decision making systems to treat certain groups unfairly. Some authors have proposed methods to overcome this. These methods include new metrics which incorporate measures of unfairly treating individuals based on group affiliation, probabilistic graphical models that assume dataset labels are inherently unfair and use dataset to infer the true fair labels as well as tree based methods that introduce new splitting criterions for fairness. We have evaluated these methods on datasets used in fairness research and evaluated if the results claimed by the authors are reproducible. Additionally, we have implemented new interpretability methods on top of the proposed methods to more explicitly explain their behaviour. We have found that some of the models do not achieve their claimed results and do not learn behaviour to achieve fairness while other models do achieve better predictions in terms of fairness by affirmative actions. This thesis show that machine learning interpretability and new machine learning models and approaches are necessary to achieve more fair decision making systems
Study on Naive Bayesian Classifier and its relation to Information Gain
Classification and clustering techniques in d ata mining are useful for a wide variety of real time applications dealing with large amount o f data. Some of the application areas of data mining are text classification, medical diagnosis, intrusion detection systems etc . The Naive Bayes Classifier techn ique is based on the Bayesian theorem and is particularly suited when the dimensionality of the inputs is high. Despite its simplicity, Naive Bayes can often outperform more sophisticated classification methods. The approach is called "naïve" because it assumes the independence between the various attribute values. Naïve Bayes classification can be viewed as both a descriptive and a predictive type of algorithm. The probabilities are descriptive and are then used to predict the class membership for a untrained data
Predicting stock market movements using network science: An information theoretic approach
A stock market is considered as one of the highly complex systems, which
consists of many components whose prices move up and down without having a
clear pattern. The complex nature of a stock market challenges us on making a
reliable prediction of its future movements. In this paper, we aim at building
a new method to forecast the future movements of Standard & Poor's 500 Index
(S&P 500) by constructing time-series complex networks of S&P 500 underlying
companies by connecting them with links whose weights are given by the mutual
information of 60-minute price movements of the pairs of the companies with the
consecutive 5,340 minutes price records. We showed that the changes in the
strength distributions of the networks provide an important information on the
network's future movements. We built several metrics using the strength
distributions and network measurements such as centrality, and we combined the
best two predictors by performing a linear combination. We found that the
combined predictor and the changes in S&P 500 show a quadratic relationship,
and it allows us to predict the amplitude of the one step future change in S&P
500. The result showed significant fluctuations in S&P 500 Index when the
combined predictor was high. In terms of making the actual index predictions,
we built ARIMA models. We found that adding the network measurements into the
ARIMA models improves the model accuracy. These findings are useful for
financial market policy makers as an indicator based on which they can
interfere with the markets before the markets make a drastic change, and for
quantitative investors to improve their forecasting models.Comment: 13 pages, 7 figures, 3 table
Detecting credit card fraud: An analysis of fraud detection techniques
Advancements in the modern age have brought many conveniences, one of those being credit cards. Providing an individual the ability to hold their entire purchasing power in the form of pocket-sized plastic cards have made credit cards the preferred method to complete financial transactions. However, these systems are not infallible and may provide criminals and other bad actors the opportunity to abuse them. Financial institutions and their customers lose billions of dollars every year to credit card fraud. To combat this issue, fraud detection systems are deployed to discover fraudulent activity after they have occurred. Such systems rely on advanced machine learning techniques and other supportive algorithms to detect and prevent fraud in the future. This work analyzes the various machine learning techniques for their ability to efficiently detect fraud and explores additional state-of-the-art techniques to assist with their performance. This work also proposes a generalized strategy to detect fraud regardless of a dataset\u27s features or unique characteristics. The high performing models discovered through this generalized strategy lay the foundation to build additional models based on state-of-the-art methods. This work expands on the issues of fraud detection, such as missing data and unbalanced datasets, and highlights models that combat these issues. Furthermore, state-of-the-art techniques, such as adapting to concept drift, are employed to combat fraud adaptation
Estimating a mixing distribution on the sphere using predictive recursion
Mixture models are commonly used when data show signs of heterogeneity and,
often, it is important to estimate the distribution of the latent variable
responsible for that heterogeneity. This is a common problem for data taking
values in a Euclidean space, but the work on mixing distribution estimation
based on directional data taking values on the unit sphere is limited. In this
paper, we propose using the predictive recursion (PR) algorithm to solve for a
mixture on a sphere. One key feature of PR is its computational efficiency.
Moreover, compared to likelihood-based methods that only support finite mixing
distribution estimates, PR is able to estimate a smooth mixing density. PR's
asymptotic consistency in spherical mixture models is established, and
simulation results showcase its benefits compared to existing likelihood-based
methods. We also show two real-data examples to illustrate how PR can be used
for goodness-of-fit testing and clustering.Comment: 26 pages, 8 figures, 2 table
Boosting the Discriminant Power of Naive Bayes
Naive Bayes has been widely used in many applications because of its
simplicity and ability in handling both numerical data and categorical data.
However, lack of modeling of correlations between features limits its
performance. In addition, noise and outliers in the real-world dataset also
greatly degrade the classification performance. In this paper, we propose a
feature augmentation method employing a stack auto-encoder to reduce the noise
in the data and boost the discriminant power of naive Bayes. The proposed stack
auto-encoder consists of two auto-encoders for different purposes. The first
encoder shrinks the initial features to derive a compact feature representation
in order to remove the noise and redundant information. The second encoder
boosts the discriminant power of the features by expanding them into a
higher-dimensional space so that different classes of samples could be better
separated in the higher-dimensional space. By integrating the proposed feature
augmentation method with the regularized naive Bayes, the discrimination power
of the model is greatly enhanced. The proposed method is evaluated on a set of
machine-learning benchmark datasets. The experimental results show that the
proposed method significantly and consistently outperforms the state-of-the-art
naive Bayes classifiers.Comment: Accepted by 2022 International Conference on Pattern Recognitio
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