5,772 research outputs found
Decision algorithms and flow graphs; a rough set approach, Journal of Telecommunications and Information Technology, 2003, nr 3
This paper concerns some relationship between Bayes’ theorem and rough sets. It is revealed that any decision algorithm satisfies Bayes’ theorem, without referring to either prior or posterior probabilities inherently associated with classical Bayesian methodology. This leads to a new simple form of this theorem, which results in new algorithms and applications.Besides, it is shown that with every decision algorithm a flow graph can be associated. Bayes’ theorem can be viewed as a flow conservation rule of information flow in the graph.Moreover, to every flow graph the Euclidean space can be assigned. Points of the space represent decisions specified by the decision algorithm, and distance between points depicts distance between decisions in the decision algorithm
On the Bayes-optimality of F-measure maximizers
The F-measure, which has originally been introduced in information retrieval,
is nowadays routinely used as a performance metric for problems such as binary
classification, multi-label classification, and structured output prediction.
Optimizing this measure is a statistically and computationally challenging
problem, since no closed-form solution exists. Adopting a decision-theoretic
perspective, this article provides a formal and experimental analysis of
different approaches for maximizing the F-measure. We start with a Bayes-risk
analysis of related loss functions, such as Hamming loss and subset zero-one
loss, showing that optimizing such losses as a surrogate of the F-measure leads
to a high worst-case regret. Subsequently, we perform a similar type of
analysis for F-measure maximizing algorithms, showing that such algorithms are
approximate, while relying on additional assumptions regarding the statistical
distribution of the binary response variables. Furthermore, we present a new
algorithm which is not only computationally efficient but also Bayes-optimal,
regardless of the underlying distribution. To this end, the algorithm requires
only a quadratic (with respect to the number of binary responses) number of
parameters of the joint distribution. We illustrate the practical performance
of all analyzed methods by means of experiments with multi-label classification
problems
A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining
Big data comes in various ways, types, shapes, forms and sizes. Indeed,
almost all areas of science, technology, medicine, public health, economics,
business, linguistics and social science are bombarded by ever increasing flows
of data begging to analyzed efficiently and effectively. In this paper, we
propose a rough idea of a possible taxonomy of big data, along with some of the
most commonly used tools for handling each particular category of bigness. The
dimensionality p of the input space and the sample size n are usually the main
ingredients in the characterization of data bigness. The specific statistical
machine learning technique used to handle a particular big data set will depend
on which category it falls in within the bigness taxonomy. Large p small n data
sets for instance require a different set of tools from the large n small p
variety. Among other tools, we discuss Preprocessing, Standardization,
Imputation, Projection, Regularization, Penalization, Compression, Reduction,
Selection, Kernelization, Hybridization, Parallelization, Aggregation,
Randomization, Replication, Sequentialization. Indeed, it is important to
emphasize right away that the so-called no free lunch theorem applies here, in
the sense that there is no universally superior method that outperforms all
other methods on all categories of bigness. It is also important to stress the
fact that simplicity in the sense of Ockham's razor non plurality principle of
parsimony tends to reign supreme when it comes to massive data. We conclude
with a comparison of the predictive performance of some of the most commonly
used methods on a few data sets.Comment: 18 pages, 2 figures 3 table
NeuroSVM: A Graphical User Interface for Identification of Liver Patients
Diagnosis of liver infection at preliminary stage is important for better
treatment. In todays scenario devices like sensors are used for detection of
infections. Accurate classification techniques are required for automatic
identification of disease samples. In this context, this study utilizes data
mining approaches for classification of liver patients from healthy
individuals. Four algorithms (Naive Bayes, Bagging, Random forest and SVM) were
implemented for classification using R platform. Further to improve the
accuracy of classification a hybrid NeuroSVM model was developed using SVM and
feed-forward artificial neural network (ANN). The hybrid model was tested for
its performance using statistical parameters like root mean square error (RMSE)
and mean absolute percentage error (MAPE). The model resulted in a prediction
accuracy of 98.83%. The results suggested that development of hybrid model
improved the accuracy of prediction. To serve the medicinal community for
prediction of liver disease among patients, a graphical user interface (GUI)
has been developed using R. The GUI is deployed as a package in local
repository of R platform for users to perform prediction.Comment: 9 pages, 6 figure
- …