108 research outputs found
Learning Opposites with Evolving Rules
The idea of opposition-based learning was introduced 10 years ago. Since then
a noteworthy group of researchers has used some notions of oppositeness to
improve existing optimization and learning algorithms. Among others,
evolutionary algorithms, reinforcement agents, and neural networks have been
reportedly extended into their opposition-based version to become faster and/or
more accurate. However, most works still use a simple notion of opposites,
namely linear (or type- I) opposition, that for each assigns its
opposite as . This, of course, is a very naive estimate of
the actual or true (non-linear) opposite , which has been
called type-II opposite in literature. In absence of any knowledge about a
function that we need to approximate, there seems to be no
alternative to the naivety of type-I opposition if one intents to utilize
oppositional concepts. But the question is if we can receive some level of
accuracy increase and time savings by using the naive opposite estimate
according to all reports in literature, what would we be able to
gain, in terms of even higher accuracies and more reduction in computational
complexity, if we would generate and employ true opposites? This work
introduces an approach to approximate type-II opposites using evolving fuzzy
rules when we first perform opposition mining. We show with multiple examples
that learning true opposites is possible when we mine the opposites from the
training data to subsequently approximate .Comment: Accepted for publication in The 2015 IEEE International Conference on
Fuzzy Systems (FUZZ-IEEE 2015), August 2-5, 2015, Istanbul, Turke
Learning Opposites Using Neural Networks
Many research works have successfully extended algorithms such as
evolutionary algorithms, reinforcement agents and neural networks using
"opposition-based learning" (OBL). Two types of the "opposites" have been
defined in the literature, namely \textit{type-I} and \textit{type-II}. The
former are linear in nature and applicable to the variable space, hence easy to
calculate. On the other hand, type-II opposites capture the "oppositeness" in
the output space. In fact, type-I opposites are considered a special case of
type-II opposites where inputs and outputs have a linear relationship. However,
in many real-world problems, inputs and outputs do in fact exhibit a nonlinear
relationship. Therefore, type-II opposites are expected to be better in
capturing the sense of "opposition" in terms of the input-output relation. In
the absence of any knowledge about the problem at hand, there seems to be no
intuitive way to calculate the type-II opposites. In this paper, we introduce
an approach to learn type-II opposites from the given inputs and their outputs
using the artificial neural networks (ANNs). We first perform \emph{opposition
mining} on the sample data, and then use the mined data to learn the
relationship between input and its opposite . We have validated
our algorithm using various benchmark functions to compare it against an
evolving fuzzy inference approach that has been recently introduced. The
results show the better performance of a neural approach to learn the
opposites. This will create new possibilities for integrating oppositional
schemes within existing algorithms promising a potential increase in
convergence speed and/or accuracy.Comment: To appear in proceedings of the 23rd International Conference on
Pattern Recognition (ICPR 2016), Cancun, Mexico, December 201
Seroprevalence of hepatitis B virus and its co-infection with hepatitis D virus and hepatitis C virus in Iranian adult population
Context: Hepatitis B virus (HBV) infection is one of the most prevalent public health problems worldwide (especially in developing countries). Aims: This study was carried out to determine the seroprevalence of HBV and its co-infection with hepatitis D (HDV) and C (HCV) viruses in the northeastern part of Iran. Setting and Design: A population-based cross-sectional study in Iran. Materials and Methods: As many as 1,850 subjects were explored for HBsAg. Anti-HDV and anti-HCV antibodies were assessed in HBsAg-positive cases. Statistical Analysis Used: Proportions were compared by Chi-square and Fisher's exact tests. Results: The mean age of subjects was 43.86 ± 11.2 years. The age- and sex-standardized prevalence for HBsAg positivity was 9.7%. It was higher in males than in females (OR: 1.28; 95% CI: 0.9-1.7). The risk of infection in singles was significantly higher than in married cases (OR: 2.13). Eight (5.8%) of HBsAg-positive cases were infected with HDV, and 17 (12.3%) were positive for anti-HCV antibody. Conclusion: This study demonstrates that the prevalence of HBsAg seropositivity in Golestan province of Iran is higher than the levels reported by WHO and previous studies from Iran. It is very important, especially for health providers and policy makers, to recognize the risk factors of HBV infection and its co-infection with HDV and HCV in this area and design effective preventive programs
Classification and Retrieval of Digital Pathology Scans: A New Dataset
In this paper, we introduce a new dataset, \textbf{Kimia Path24}, for image
classification and retrieval in digital pathology. We use the whole scan images
of 24 different tissue textures to generate 1,325 test patches of size
10001000 (0.5mm0.5mm). Training data can be generated according
to preferences of algorithm designer and can range from approximately 27,000 to
over 50,000 patches if the preset parameters are adopted. We propose a compound
patch-and-scan accuracy measurement that makes achieving high accuracies quite
challenging. In addition, we set the benchmarking line by applying LBP,
dictionary approach and convolutional neural nets (CNNs) and report their
results. The highest accuracy was 41.80\% for CNN.Comment: Accepted for presentation at Workshop for Computer Vision for
Microscopy Image Analysis (CVMI 2017) @ CVPR 2017, Honolulu, Hawai
Ranking Loss and Sequestering Learning for Reducing Image Search Bias in Histopathology
Recently, deep learning has started to play an essential role in healthcare
applications, including image search in digital pathology. Despite the recent
progress in computer vision, significant issues remain for image searching in
histopathology archives. A well-known problem is AI bias and lack of
generalization. A more particular shortcoming of deep models is the ignorance
toward search functionality. The former affects every model, the latter only
search and matching. Due to the lack of ranking-based learning, researchers
must train models based on the classification error and then use the resultant
embedding for image search purposes. Moreover, deep models appear to be prone
to internal bias even if using a large image repository of various hospitals.
This paper proposes two novel ideas to improve image search performance. First,
we use a ranking loss function to guide feature extraction toward the
matching-oriented nature of the search. By forcing the model to learn the
ranking of matched outputs, the representation learning is customized toward
image search instead of learning a class label. Second, we introduce the
concept of sequestering learning to enhance the generalization of feature
extraction. By excluding the images of the input hospital from the matched
outputs, i.e., sequestering the input domain, the institutional bias is
reduced. The proposed ideas are implemented and validated through the largest
public dataset of whole slide images. The experiments demonstrate superior
results compare to the-state-of-art.Comment: Under Review for publicatio
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