60,337 research outputs found
Classification of Message Spreading in a Heterogeneous Social Network
Nowadays, social networks such as Twitter, Facebook and LinkedIn become
increasingly popular. In fact, they introduced new habits, new ways of
communication and they collect every day several information that have
different sources. Most existing research works fo-cus on the analysis of
homogeneous social networks, i.e. we have a single type of node and link in the
network. However, in the real world, social networks offer several types of
nodes and links. Hence, with a view to preserve as much information as
possible, it is important to consider so-cial networks as heterogeneous and
uncertain. The goal of our paper is to classify the social message based on its
spreading in the network and the theory of belief functions. The proposed
classifier interprets the spread of messages on the network, crossed paths and
types of links. We tested our classifier on a real word network that we
collected from Twitter, and our experiments show the performance of our belief
classifier
Basic tasks of sentiment analysis
Subjectivity detection is the task of identifying objective and subjective
sentences. Objective sentences are those which do not exhibit any sentiment.
So, it is desired for a sentiment analysis engine to find and separate the
objective sentences for further analysis, e.g., polarity detection. In
subjective sentences, opinions can often be expressed on one or multiple
topics. Aspect extraction is a subtask of sentiment analysis that consists in
identifying opinion targets in opinionated text, i.e., in detecting the
specific aspects of a product or service the opinion holder is either praising
or complaining about
On Cognitive Preferences and the Plausibility of Rule-based Models
It is conventional wisdom in machine learning and data mining that logical
models such as rule sets are more interpretable than other models, and that
among such rule-based models, simpler models are more interpretable than more
complex ones. In this position paper, we question this latter assumption by
focusing on one particular aspect of interpretability, namely the plausibility
of models. Roughly speaking, we equate the plausibility of a model with the
likeliness that a user accepts it as an explanation for a prediction. In
particular, we argue that, all other things being equal, longer explanations
may be more convincing than shorter ones, and that the predominant bias for
shorter models, which is typically necessary for learning powerful
discriminative models, may not be suitable when it comes to user acceptance of
the learned models. To that end, we first recapitulate evidence for and against
this postulate, and then report the results of an evaluation in a
crowd-sourcing study based on about 3.000 judgments. The results do not reveal
a strong preference for simple rules, whereas we can observe a weak preference
for longer rules in some domains. We then relate these results to well-known
cognitive biases such as the conjunction fallacy, the representative heuristic,
or the recogition heuristic, and investigate their relation to rule length and
plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus
on plausibility and relation to interpretability, comprehensibility, and
justifiabilit
Deep Adaptive Feature Embedding with Local Sample Distributions for Person Re-identification
Person re-identification (re-id) aims to match pedestrians observed by
disjoint camera views. It attracts increasing attention in computer vision due
to its importance to surveillance system. To combat the major challenge of
cross-view visual variations, deep embedding approaches are proposed by
learning a compact feature space from images such that the Euclidean distances
correspond to their cross-view similarity metric. However, the global Euclidean
distance cannot faithfully characterize the ideal similarity in a complex
visual feature space because features of pedestrian images exhibit unknown
distributions due to large variations in poses, illumination and occlusion.
Moreover, intra-personal training samples within a local range are robust to
guide deep embedding against uncontrolled variations, which however, cannot be
captured by a global Euclidean distance. In this paper, we study the problem of
person re-id by proposing a novel sampling to mine suitable \textit{positives}
(i.e. intra-class) within a local range to improve the deep embedding in the
context of large intra-class variations. Our method is capable of learning a
deep similarity metric adaptive to local sample structure by minimizing each
sample's local distances while propagating through the relationship between
samples to attain the whole intra-class minimization. To this end, a novel
objective function is proposed to jointly optimize similarity metric learning,
local positive mining and robust deep embedding. This yields local
discriminations by selecting local-ranged positive samples, and the learned
features are robust to dramatic intra-class variations. Experiments on
benchmarks show state-of-the-art results achieved by our method.Comment: Published on Pattern Recognitio
A survey on utilization of data mining approaches for dermatological (skin) diseases prediction
Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data
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