مجال
التميز
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إبداع علمي + جائزة تفوقية + تميز دراسي
وبحثي
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جوائز التكريم:
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الجائزة (1):
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مسمى الجائزة:
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Best Paper presentation
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الجهة المانحة:
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8th
Saudi Student Conference organising committee
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تاريخ الجائزة:
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01 Feb 2015
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مجال التكريم:
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Machine Learning, Data Mining &
Visualization
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البحوث المنشورة
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البحث (1):
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عنوان البحث:
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Feature Construction and Calibration for Clustering
Daily Load Curves from Smart Meter Data
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رابط إلى البحث:
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Click here
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تاريخ النشر:
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11/02/2016
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موجز عن البحث:
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This paper proposes and compares feature
construction and calibration methods for clustering daily electricity load
curves. Such load curves describe electricity demand over a period of time. A
rich body of the literature has studied clustering of load curves, usually
using temporal features. This limits the potential to discover new knowledge
which may not be best represented as models consisting of all time points on
load curves. This paper presents three new methods to construct features:
conditional filters on time-resolution based features, calibration and
normalization, and using profile errors. These new features extend the
potential of clustering load curves. Moreover, smart metering is now
generating high-resolution time series, and so the dimensionality reduction
offered by these features is welcome. The clustering results using the
proposed new features are compared with clusterings obtained from temporal
features as well as clusterings with Fourier features, using household electricity
consumption time series as test data. The experimental results suggest that
the proposed feature construction methods offer new means for gaining insight
in energy consumption patterns.
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المؤتمرات العلمية:
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المؤتمر (1):
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عنوان المؤتمر:
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International Conference Machine Learning
and Applications
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تاريخ الإنعقاد:
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03/12/2014
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مكان
الإنعقاد:
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Detroit, USA
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طبيعة المشاركة:
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Paper presentation
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عنوان المشاركة:
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LaCova: A Tree-Based Multi-Label Classifier using
Label Covariance as Splitting Criterion
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ملخص المشاركة:
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Dealing with multiple labels is a supervised
learning problem of increasing importance. Multi-label classifiers face the
challenge of exploiting correlations between labels. While in existing work these
correlations are often modelled globally, in this paper we use the
divide-and-conquer approach of decision trees which enables taking local
decisions about how best to model label dependency. The resulting algorithm
establishes a tree-based multi-label classifier called LaCova which
dynamically interpolates between two well-known baseline methods: Binary
Relevance, which assumes all labels independent, and Label Powerset, which
learns the joint label distribution. The key idea is a splitting criterion
based on the label covariance matrix at that node, which allows us to choose
between a horizontal split (branching on a feature) and a vertical split
(separating the labels). Empirical results on 12 data sets show strong
performance of the proposed method, particularly on data sets with hundreds
of labels.
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المؤتمر (2):
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عنوان المؤتمر:
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8th
Saudi Students’ Conference
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تاريخ الإنعقاد:
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31/01/2015
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مكان
الإنعقاد:
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London,
UK
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طبيعة المشاركة:
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Paper presentation
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عنوان المشاركة:
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Hybrid
multi-label decision trees for classification
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ملخص المشاركة:
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Multi-label classification is a supervised learning
problem that predicts multiple labels simultaneously. One of the key
challenges in such tasks is modelling the correlations between multiple
labels. In this paper we use decision trees algorithm, which models label
dependency locally. The proposed algorithm establishes a Hybrid Multi-Label
Decision Trees called HMLDT that interpolates between two baseline methods:
Binary Relevance, which assumes all labels independent, and Label Powerset,
which learns the joint label distribution. The key idea is a splitting
criterion based on the label covariance matrix. Empirical results on 12 data
sets show strong performance of the HMLDT, particularly on data sets with
hundreds of labels.
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المؤتمر (3):
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عنوان المؤتمر:
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Doctoral Consortium at the European Conference on
Machine Learning and Principles and Practice of Knowledge Discovery in
Databases
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تاريخ الإنعقاد:
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07/09/2015
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مكان
الإنعقاد:
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Porto, Portugal
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طبيعة المشاركة:
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Poster presentation
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عنوان المشاركة:
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Multi-label Classification by Label Clustering based
on Covariance
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ملخص المشاركة:
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Multi-label classification is a supervised learning
problem that predicts multiple labels simultaneously. One of the key
challenges in such tasks is modelling the correlations between multiple
labels. LaCova is a decision tree multi-label classifier that interpolates
between two baseline methods: Binary Rel- evance (BR), which assumes all
labels independent; and Label Powerset (LP), which learns the joint label
distribution. In this paper we introduce LaCova- CLus that clusters labels
into several dependent subsets as an additional splitting criterion. Clusters
are obtained locally by identifying the connected components in the
thresholded absolute covariance matrix. The proposed algorithm is evaluated
and compared to baseline and state-of-the-art approaches. Experimental
results show that our method can improve the label exact-match.
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المؤتمر (4):
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عنوان المؤتمر:
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The European Conference on Machine Learning and
Principles and Practice of Knowledge Discovery in Databases
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تاريخ الإنعقاد:
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07/09/2015
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مكان
الإنعقاد:
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Porto, Portugal
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طبيعة المشاركة:
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Paper presentation
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عنوان المشاركة:
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Versatile Decision Trees for Learning Over Multiple
Contexts
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ملخص المشاركة:
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Discriminative models for classification assume that
training and deployment data are drawn from the same distribution. The
performance of these models can vary significantly when they are learned and
deployed in different contexts with different data distributions. In the
literature, this phenomenon is called dataset shift. In this paper, we
address several important issues in the dataset shift problem. First, how can
we automatically detect that there is a significant difference between
training and deployment data to take action or adjust the model
appropriately? Secondly, different shifts can occur in real applications
(e.g., linear and non-linear), which require the use of diverse solutions.
Thirdly, how should we combine the original model of the training data with
other models to achieve better performance? This work offers two main
contributions towards these issues. We propose a Versatile Model that is rich
enough to handle different kinds of shift without making strong assumptions
such as linearity, and furthermore does not require labelled data to identify
the data shift at deployment. Empirical results on both synthetic shift and
real datasets shift show strong performance gains by achieved the proposed
model.
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المؤتمر (5):
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عنوان المؤتمر:
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9th Saudi Students’
Conference
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تاريخ الإنعقاد:
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13/02/2016
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مكان
الإنعقاد:
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Birmingham,
UK
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طبيعة المشاركة:
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Paper presentation
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عنوان المشاركة:
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A Context-Aware Model based on Versatile Decision
Trees
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ملخص المشاركة:
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One of the assumptions in many supervised machine
learning algorithms is that both training and deployment data follow the same
distribution. In violation of this assumption, for example, the model is
built using data from one country but the predictions are collected in
another country, the trained model cannot be applied to the deployment data
and re-training a new model is necessary. This paper proposes a context-aware
model, which is called the Versatile Model (VM). The VM basically is a
decision tree model that is adaptable to different kinds of shift without
making strong assumptions such as the linear relationship. The VM adopts
non-parametric statistical test called Kolmogorov-Smirnov (KS) test to detect
the significant difference between training and deployment data. Experimental
results on both synthetic and non-synthetic datasets shift prove the strong
performance of the VM
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المؤتمر (6):
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عنوان المؤتمر:
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First International Workshop on Learning over
Multiple Contexts in conjunction with the European Conference on Machine
Learning and Principles and Practice of Knowledge Discovery in Databases
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تاريخ الإنعقاد:
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15/09/2014
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مكان
الإنعقاد:
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Nancy, France
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طبيعة المشاركة:
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Paper presentation
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عنوان المشاركة:
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Multi-label Classification: A Comparative Study on
Threshold Selection Methods
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ملخص المشاركة:
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Dealing with multiple labels is a supervised
learning problem of in- creasing importance. However, in some tasks, certain
learning algorithms produce a confidence score vector for each label that
needs to be classified as relevant or irrelevant. More importantly,
multi-label models are learnt in training conditions called operating
conditions, which most likely change in other contexts. In this work, we
explore the existing thresholding methods of multi-label classification by
considering that label costs are operating conditions. This paper provides an
empirical comparative study of these approaches by calculating the empirical
loss over range of operating conditions. It also contributes two new methods
in multi- label classification that have been used in binary classification:
score-driven and one optimal.
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المؤتمر (7):
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عنوان المؤتمر:
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The biennial European Conference on
Artificial Intelligence (ECAI)
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تاريخ الإنعقاد:
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02/09/2016
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مكان
الإنعقاد:
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The Hague, Netherlands
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طبيعة المشاركة:
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Paper presentation
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عنوان المشاركة:
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Declaratively Capturing Local Label
Correlations with Multi-Label Trees
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ملخص المشاركة:
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The goal of multi-label classification is to predict
multiple labels per data point simultaneously. Real-world applications tend
to have high-dimensional label spaces, employing hundreds or even thousands
of labels. While these labels could be predicted separately, by capturing
label correlation we might achieve better predictive performance. In contrast
with previous attempts in the literature that have modelled label correlations
globally, this paper proposes a novel algorithm to model correlations and
cluster labels locally. La- CovaC is a multi-label decision tree classifier
that clusters labels into several dependent subsets at various points during
training. The clusters are obtained locally by identifying the
conditionally-dependent labels in localised regions of the feature space
using the label correlation matrix. LaCovaC interleaves between two main
decisions on the label matrix with training instances in rows and labels in
columns: splitting this matrix vertically by partitioning the labels into
subsets, or splitting it horizontally using features in the conventional way.
Experiments on 13 benchmark datasets demonstrate that our proposal achieves
competitive performance over a wide range of evaluation metrics when compared
with the state-of-the-art multi-label classifiers.
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