4,911 research outputs found
Validation of Soft Classification Models using Partial Class Memberships: An Extended Concept of Sensitivity & Co. applied to the Grading of Astrocytoma Tissues
We use partial class memberships in soft classification to model uncertain
labelling and mixtures of classes. Partial class memberships are not restricted
to predictions, but may also occur in reference labels (ground truth, gold
standard diagnosis) for training and validation data.
Classifier performance is usually expressed as fractions of the confusion
matrix, such as sensitivity, specificity, negative and positive predictive
values. We extend this concept to soft classification and discuss the bias and
variance properties of the extended performance measures. Ambiguity in
reference labels translates to differences between best-case, expected and
worst-case performance. We show a second set of measures comparing expected and
ideal performance which is closely related to regression performance, namely
the root mean squared error RMSE and the mean absolute error MAE.
All calculations apply to classical crisp classification as well as to soft
classification (partial class memberships and/or one-class classifiers). The
proposed performance measures allow to test classifiers with actual borderline
cases. In addition, hardening of e.g. posterior probabilities into class labels
is not necessary, avoiding the corresponding information loss and increase in
variance.
We implement the proposed performance measures in the R package
"softclassval", which is available from CRAN and at
http://softclassval.r-forge.r-project.org.
Our reasoning as well as the importance of partial memberships for
chemometric classification is illustrated by a real-word application:
astrocytoma brain tumor tissue grading (80 patients, 37000 spectra) for finding
surgical excision borders. As borderline cases are the actual target of the
analytical technique, samples which are diagnosed to be borderline cases must
be included in the validation.Comment: The manuscript is accepted for publication in Chemometrics and
Intelligent Laboratory Systems. Supplementary figures and tables are at the
end of the pd
Laplacian Mixture Modeling for Network Analysis and Unsupervised Learning on Graphs
Laplacian mixture models identify overlapping regions of influence in
unlabeled graph and network data in a scalable and computationally efficient
way, yielding useful low-dimensional representations. By combining Laplacian
eigenspace and finite mixture modeling methods, they provide probabilistic or
fuzzy dimensionality reductions or domain decompositions for a variety of input
data types, including mixture distributions, feature vectors, and graphs or
networks. Provable optimal recovery using the algorithm is analytically shown
for a nontrivial class of cluster graphs. Heuristic approximations for scalable
high-performance implementations are described and empirically tested.
Connections to PageRank and community detection in network analysis demonstrate
the wide applicability of this approach. The origins of fuzzy spectral methods,
beginning with generalized heat or diffusion equations in physics, are reviewed
and summarized. Comparisons to other dimensionality reduction and clustering
methods for challenging unsupervised machine learning problems are also
discussed.Comment: 13 figures, 35 reference
The Prototyping and Focused Discriminating Strategy for Pattern Recognition and one Instantiation: the MELIDIS System
This paper presents the Prototyping and Focused Discriminating (PFD) strategy for pattern recognition. This strategy takes benefits from the duality between model generation and discrimination. Both collaborate through a focusing mechanism that detects the conflicts between the class models and drive the discrimination. Classifiers based on this collaboration benefit from a set of useful properties. The MĂ©lidis system illustrates this strategy and extends its possibilities, using a fuzzy framework. As shown by experiments, the resulting system provides an interesting compromise between accuracy and compactness. Experiments also demonstrate the interest of the new strategy and of its focusing mechanism
One-Class Classification: Taxonomy of Study and Review of Techniques
One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.Comment: 24 pages + 11 pages of references, 8 figure
Deep Generative Models for Reject Inference in Credit Scoring
Credit scoring models based on accepted applications may be biased and their
consequences can have a statistical and economic impact. Reject inference is
the process of attempting to infer the creditworthiness status of the rejected
applications. In this research, we use deep generative models to develop two
new semi-supervised Bayesian models for reject inference in credit scoring, in
which we model the data generating process to be dependent on a Gaussian
mixture. The goal is to improve the classification accuracy in credit scoring
models by adding reject applications. Our proposed models infer the unknown
creditworthiness of the rejected applications by exact enumeration of the two
possible outcomes of the loan (default or non-default). The efficient
stochastic gradient optimization technique used in deep generative models makes
our models suitable for large data sets. Finally, the experiments in this
research show that our proposed models perform better than classical and
alternative machine learning models for reject inference in credit scoring
Combining case based reasoning with neural networks
This paper presents a neural network based technique for mapping problem situations to problem solutions for Case-Based Reasoning (CBR) applications. Both neural networks and
CBR are instance-based learning techniques, although neural nets work with numerical data and CBR systems work with symbolic data. This paper discusses how the application scope of both paradigms could be enhanced by the use of hybrid concepts. To make the use of neural networks possible, the problem's situation and solution features are transformed into continuous features, using techniques similar to CBR's definition of similarity metrics. Radial Basis Function (RBF) neural nets are used to create a multivariable, continuous input-output mapping. As the mapping is continuous, this technique also provides generalisation between cases, replacing the domain specific
solution adaptation techniques required by conventional CBR. This continuous representation also allows, as in
fuzzy logic, an associated membership measure to be output with each symbolic feature, aiding the prioritisation of various possible solutions. A further advantage is that, as the RBF neurons are only active in a limited area of the input space, the solution can be accompanied by local estimates of accuracy, based on the sufficiency of the cases present in that area as well as the results measured during testing. We describe how the application of this technique could be of benefit to the real world problem of sales advisory systems, among others
- …