1,723 research outputs found
Using Clustering Method to Understand Indian Stock Market Volatility
In this paper we use Clustering Method to understand whether stock market
volatility can be predicted at all, and if so, when it can be predicted. The
exercise has been performed for the Indian stock market on daily data for two
years. For our analysis we map number of clusters against number of variables.
We then test for efficiency of clustering. Our contention is that, given a
fixed number of variables, one of them being historic volatility of NIFTY
returns, if increase in the number of clusters improves clustering efficiency,
then volatility cannot be predicted. Volatility then becomes random as, for a
given time period, it gets classified in various clusters. On the other hand,
if efficiency falls with increase in the number of clusters, then volatility
can be predicted as there is some homogeneity in the data. If we fix the number
of clusters and then increase the number of variables, this should have some
impact on clustering efficiency. Indeed if we can hit upon, in a sense, an
optimum number of variables, then if the number of clusters is reasonably
small, we can use these variables to predict volatility. The variables that we
consider for our study are volatility of NIFTY returns, volatility of gold
returns, India VIX, CBOE VIX, volatility of crude oil returns, volatility of
DJIA returns, volatility of DAX returns, volatility of Hang Seng returns and
volatility of Nikkei returns. We use three clustering algorithms namely Kernel
K-Means, Self Organizing Maps and Mixture of Gaussian models and two internal
clustering validity measures, Silhouette Index and Dunn Index, to assess the
quality of generated clusters
Mapping Auto-context Decision Forests to Deep ConvNets for Semantic Segmentation
We consider the task of pixel-wise semantic segmentation given a small set of
labeled training images. Among two of the most popular techniques to address
this task are Decision Forests (DF) and Neural Networks (NN). In this work, we
explore the relationship between two special forms of these techniques: stacked
DFs (namely Auto-context) and deep Convolutional Neural Networks (ConvNet). Our
main contribution is to show that Auto-context can be mapped to a deep ConvNet
with novel architecture, and thereby trained end-to-end. This mapping can be
used as an initialization of a deep ConvNet, enabling training even in the face
of very limited amounts of training data. We also demonstrate an approximate
mapping back from the refined ConvNet to a second stacked DF, with improved
performance over the original. We experimentally verify that these mappings
outperform stacked DFs for two different applications in computer vision and
biology: Kinect-based body part labeling from depth images, and somite
segmentation in microscopy images of developing zebrafish. Finally, we revisit
the core mapping from a Decision Tree (DT) to a NN, and show that it is also
possible to map a fuzzy DT, with sigmoidal split decisions, to a NN. This
addresses multiple limitations of the previous mapping, and yields new insights
into the popular Rectified Linear Unit (ReLU), and more recently proposed
concatenated ReLU (CReLU), activation functions
A hybrid model for bankruptcy prediction using genetic algorithm, fuzzy c-means and mars
Bankruptcy prediction is very important for all the organization since it
affects the economy and rise many social problems with high costs. There are
large number of techniques have been developed to predict the bankruptcy, which
helps the decision makers such as investors and financial analysts. One of the
bankruptcy prediction models is the hybrid model using Fuzzy C-means clustering
and MARS, which uses static ratios taken from the bank financial statements for
prediction, which has its own theoretical advantages. The performance of
existing bankruptcy model can be improved by selecting the best features
dynamically depend on the nature of the firm. This dynamic selection can be
accomplished by Genetic Algorithm and it improves the performance of prediction
model.Comment: Bankruptcy prediction, financial ratio models, Genetic Algorithm,
Fuzzy c-means Clustering, MAR
End-to-end Learning of Deterministic Decision Trees
Conventional decision trees have a number of favorable properties, including
interpretability, a small computational footprint and the ability to learn from
little training data. However, they lack a key quality that has helped fuel the
deep learning revolution: that of being end-to-end trainable, and to learn from
scratch those features that best allow to solve a given supervised learning
problem. Recent work (Kontschieder 2015) has addressed this deficit, but at the
cost of losing a main attractive trait of decision trees: the fact that each
sample is routed along a small subset of tree nodes only. We here propose a
model and Expectation-Maximization training scheme for decision trees that are
fully probabilistic at train time, but after a deterministic annealing process
become deterministic at test time. We also analyze the learned oblique split
parameters on image datasets and show that Neural Networks can be trained at
each split node. In summary, we present the first end-to-end learning scheme
for deterministic decision trees and present results on par with or superior to
published standard oblique decision tree algorithms
Truecluster: robust scalable clustering with model selection
Data-based classification is fundamental to most branches of science. While
recent years have brought enormous progress in various areas of statistical
computing and clustering, some general challenges in clustering remain: model
selection, robustness, and scalability to large datasets. We consider the
important problem of deciding on the optimal number of clusters, given an
arbitrary definition of space and clusteriness. We show how to construct a
cluster information criterion that allows objective model selection. Differing
from other approaches, our truecluster method does not require specific
assumptions about underlying distributions, dissimilarity definitions or
cluster models. Truecluster puts arbitrary clustering algorithms into a generic
unified (sampling-based) statistical framework. It is scalable to big datasets
and provides robust cluster assignments and case-wise diagnostics. Truecluster
will make clustering more objective, allows for automation, and will save time
and costs. Free R software is available.Comment: Article (10 figures). Changes in 2nd version: dropped supplements in
favor of better integrated presentation, better literature coverage, put into
proper English. Author's website available via http://www.truecluster.co
Segmentation of articular cartilage and early osteoarthritis based on the fuzzy soft thresholding approach driven by modified evolutionary ABC optimization and local statistical aggregation
Articular cartilage assessment, with the aim of the cartilage loss identification, is a crucial task for the clinical practice of orthopedics. Conventional software (SW) instruments allow for just a visualization of the knee structure, without post processing, offering objective cartilage modeling. In this paper, we propose the multiregional segmentation method, having ambitions to bring a mathematical model reflecting the physiological cartilage morphological structure and spots, corresponding with the early cartilage loss, which is poorly recognizable by the naked eye from magnetic resonance imaging (MRI). The proposed segmentation model is composed from two pixel's classification parts. Firstly, the image histogram is decomposed by using a sequence of the triangular fuzzy membership functions, when their localization is driven by the modified artificial bee colony (ABC) optimization algorithm, utilizing a random sequence of considered solutions based on the real cartilage features. In the second part of the segmentation model, the original pixel's membership in a respective segmentation class may be modified by using the local statistical aggregation, taking into account the spatial relationships regarding adjacent pixels. By this way, the image noise and artefacts, which are commonly presented in the MR images, may be identified and eliminated. This fact makes the model robust and sensitive with regards to distorting signals. We analyzed the proposed model on the 2D spatial MR image records. We show different MR clinical cases for the articular cartilage segmentation, with identification of the cartilage loss. In the final part of the analysis, we compared our model performance against the selected conventional methods in application on the MR image records being corrupted by additive image noise.Web of Science117art. no. 86
Survey of state-of-the-art mixed data clustering algorithms
Mixed data comprises both numeric and categorical features, and mixed
datasets occur frequently in many domains, such as health, finance, and
marketing. Clustering is often applied to mixed datasets to find structures and
to group similar objects for further analysis. However, clustering mixed data
is challenging because it is difficult to directly apply mathematical
operations, such as summation or averaging, to the feature values of these
datasets. In this paper, we present a taxonomy for the study of mixed data
clustering algorithms by identifying five major research themes. We then
present a state-of-the-art review of the research works within each research
theme. We analyze the strengths and weaknesses of these methods with pointers
for future research directions. Lastly, we present an in-depth analysis of the
overall challenges in this field, highlight open research questions and discuss
guidelines to make progress in the field.Comment: 20 Pages, 2 columns, 6 Tables, 209 Reference
Learning from Imprecise and Fuzzy Observations: Data Disambiguation through Generalized Loss Minimization
Methods for analyzing or learning from "fuzzy data" have attracted increasing
attention in recent years. In many cases, however, existing methods (for
precise, non-fuzzy data) are extended to the fuzzy case in an ad-hoc manner,
and without carefully considering the interpretation of a fuzzy set when being
used for modeling data. Distinguishing between an ontic and an epistemic
interpretation of fuzzy set-valued data, and focusing on the latter, we argue
that a "fuzzification" of learning algorithms based on an application of the
generic extension principle is not appropriate. In fact, the extension
principle fails to properly exploit the inductive bias underlying statistical
and machine learning methods, although this bias, at least in principle, offers
a means for "disambiguating" the fuzzy data. Alternatively, we therefore
propose a method which is based on the generalization of loss functions in
empirical risk minimization, and which performs model identification and data
disambiguation simultaneously. Elaborating on the fuzzification of specific
types of losses, we establish connections to well-known loss functions in
regression and classification. We compare our approach with related methods and
illustrate its use in logistic regression for binary classification
Numeric Input Relations for Relational Learning with Applications to Community Structure Analysis
Most work in the area of statistical relational learning (SRL) is focussed on
discrete data, even though a few approaches for hybrid SRL models have been
proposed that combine numerical and discrete variables. In this paper we
distinguish numerical random variables for which a probability distribution is
defined by the model from numerical input variables that are only used for
conditioning the distribution of discrete response variables. We show how
numerical input relations can very easily be used in the Relational Bayesian
Network framework, and that existing inference and learning methods need only
minor adjustments to be applied in this generalized setting. The resulting
framework provides natural relational extensions of classical probabilistic
models for categorical data. We demonstrate the usefulness of RBN models with
numeric input relations by several examples.
In particular, we use the augmented RBN framework to define probabilistic
models for multi-relational (social) networks in which the probability of a
link between two nodes depends on numeric latent feature vectors associated
with the nodes. A generic learning procedure can be used to obtain a
maximum-likelihood fit of model parameters and latent feature values for a
variety of models that can be expressed in the high-level RBN representation.
Specifically, we propose a model that allows us to interpret learned latent
feature values as community centrality degrees by which we can identify nodes
that are central for one community, that are hubs between communities, or that
are isolated nodes. In a multi-relational setting, the model also provides a
characterization of how different relations are associated with each community
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