11 research outputs found
End-to-end Feature Selection Approach for Learning Skinny Trees
Joint feature selection and tree ensemble learning is a challenging task.
Popular tree ensemble toolkits e.g., Gradient Boosted Trees and Random Forests
support feature selection post-training based on feature importances, which are
known to be misleading, and can significantly hurt performance. We propose
Skinny Trees: a toolkit for feature selection in tree ensembles, such that
feature selection and tree ensemble learning occurs simultaneously. It is based
on an end-to-end optimization approach that considers feature selection in
differentiable trees with Group regularization. We optimize
with a first-order proximal method and present convergence guarantees for a
non-convex and non-smooth objective. Interestingly, dense-to-sparse
regularization scheduling can lead to more expressive and sparser tree
ensembles than vanilla proximal method. On 15 synthetic and real-world
datasets, Skinny Trees can achieve - feature
compression rates, leading up to faster inference over dense trees,
without any loss in performance. Skinny Trees lead to superior feature
selection than many existing toolkits e.g., in terms of AUC performance for
feature budget, Skinny Trees outperforms LightGBM by (up to
), and Random Forests by (up to ).Comment: Preprin
Modélisation formelle des systèmes de détection d'intrusions
L’écosystème de la cybersécurité évolue en permanence en termes du nombre, de la diversité, et de la complexité des attaques. De ce fait, les outils de détection deviennent inefficaces face à certaines attaques. On distingue généralement trois types de systèmes de détection d’intrusions : détection par anomalies, détection par signatures et détection hybride. La détection par anomalies est fondée sur la caractérisation du comportement habituel du système, typiquement de manière statistique. Elle permet de détecter des attaques connues ou inconnues, mais génère aussi un très grand nombre de faux positifs. La détection par signatures permet de détecter des attaques connues en définissant des règles qui décrivent le comportement connu d’un attaquant. Cela demande une bonne connaissance du comportement de l’attaquant. La détection hybride repose sur plusieurs méthodes de détection incluant celles sus-citées. Elle présente l’avantage d’être plus précise pendant la détection. Des outils tels que Snort et Zeek offrent des langages de bas niveau pour l’expression de règles de reconnaissance d’attaques. Le nombre d’attaques potentielles étant très grand, ces bases de règles deviennent rapidement difficiles à gérer et à maintenir. De plus, l’expression de règles avec état dit stateful est particulièrement ardue pour reconnaître une séquence d’événements. Dans cette thèse, nous proposons une approche stateful basée sur les diagrammes d’état-transition algébriques (ASTDs) afin d’identifier des attaques complexes. Les ASTDs permettent de représenter de façon graphique et modulaire une spécification, ce qui facilite la maintenance et la compréhension des règles. Nous étendons la notation ASTD avec de nouvelles fonctionnalités pour représenter des attaques complexes. Ensuite, nous spécifions plusieurs attaques avec la notation étendue et exécutons les spécifications obtenues sur des flots d’événements à l’aide d’un interpréteur pour identifier des attaques. Nous évaluons aussi les performances de l’interpréteur avec des outils industriels tels que Snort et Zeek. Puis, nous réalisons un compilateur afin de générer du code exécutable à partir d’une spécification ASTD, capable d’identifier de façon efficiente les séquences d’événements.Abstract : The cybersecurity ecosystem continuously evolves with the number, the diversity,
and the complexity of cyber attacks. Generally, we have three types of Intrusion
Detection System (IDS) : anomaly-based detection, signature-based detection, and
hybrid detection. Anomaly detection is based on the usual behavior description of
the system, typically in a static manner. It enables detecting known or unknown attacks
but also generating a large number of false positives. Signature based detection
enables detecting known attacks by defining rules that describe known attacker’s behavior.
It needs a good knowledge of attacker behavior. Hybrid detection relies on
several detection methods including the previous ones. It has the advantage of being
more precise during detection. Tools like Snort and Zeek offer low level languages to
represent rules for detecting attacks. The number of potential attacks being large,
these rule bases become quickly hard to manage and maintain. Moreover, the representation
of stateful rules to recognize a sequence of events is particularly arduous. In this thesis, we propose a stateful approach based on algebraic state-transition
diagrams (ASTDs) to identify complex attacks. ASTDs allow a graphical and modular
representation of a specification, that facilitates maintenance and understanding of
rules. We extend the ASTD notation with new features to represent complex attacks.
Next, we specify several attacks with the extended notation and run the resulting specifications
on event streams using an interpreter to identify attacks. We also evaluate
the performance of the interpreter with industrial tools such as Snort and Zeek. Then,
we build a compiler in order to generate executable code from an ASTD specification,
able to efficiently identify sequences of events
Advances in Artificial Intelligence: Models, Optimization, and Machine Learning
The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications
Coverage & cooperation: Completing complex tasks as quickly as possible using teams of robots
As the robotics industry grows and robots enter our homes and public spaces, they are increasingly expected to work in cooperation with each other. My thesis focuses on multirobot planning, specifically in the context of coverage robots, such as robotic lawnmowers and vacuum cleaners.
Two problems unique to multirobot teams are task allocation and search. I present a task allocation algorithm which balances the workload amongst all robots in the team with the objective of minimizing the overall mission time. I also present a search algorithm which robots can use to find lost teammates. It uses a probabilistic belief of a target robot’s position to create a planning tree and then searches by following the best path in the tree.
For robust multirobot coverage, I use both the task allocation and search algorithms. First the coverage region is divided into a set of small coverage tasks which minimize the number of turns the robots will need to take. These tasks are then allocated to individual robots. During the mission, robots replan with nearby robots to rebalance the workload and, once a robot has finished its tasks, it searches for teammates to help them finish their tasks faster
Multi-Objective Optimization in Metabolomics/Computational Intelligence
The development of reliable computational models for detecting non-linear patterns
encased in throughput datasets and characterizing them into phenotypic classes
has been of particular interest and comprises dynamic studies in metabolomics
and other disciplines that are encompassed within the omics science. Some of the
clinical conditions that have been associated with these studies include metabotypes
in cancer, in
ammatory bowel disease (IBD), asthma, diabetes, traumatic brain
injury (TBI), metabolic syndrome, and Parkinson's disease, just to mention a few.
The traction in this domain is attributable to the advancements in the procedures
involved in 1H NMR-linked datasets acquisition, which have fuelled the generation of
a wide abundance of datasets. Throughput datasets generated by modern 1H NMR
spectrometers are often characterized with features that are uninformative, redundant
and inherently correlated. This renders it di cult for conventional multivariate
analysis techniques to e ciently capture important signals and patterns. Therefore,
the work covered in this research thesis provides novel alternative techniques to
address the limitations of current analytical pipelines. This work delineates 13 variants
of population-based nature inspired metaheuristic optimization algorithms which
were further developed in this thesis as wrapper-based feature selection optimizers.
The optimizers were then evaluated and benchmarked against each other through
numerical experiments. Large-scale 1H NMR-linked datasets emerging from three
disease studies were employed for the evaluations. The rst is a study in patients
diagnosed with Malan syndrome; an autosomal dominant inherited disorder marked
by a distinctive facial appearance, learning disabilities, and gigantism culminating
in tall stature and macrocephaly, also referred to as cerebral gigantism. Another
study involved Niemann-Pick Type C1 (NP-C1), a rare progressive neurodegenerative
condition marked by intracellular accrual of cholesterol and complex lipids including
sphingolipids and phospholipids in the endosomal/lysosomal system. The third
study involved sore throat investigation in human (also known as `pharyngitis'); an
acute infection of the upper respiratory tract that a ects the respiratory mucosa
of the throat. In all three cases, samples from pathologically-con rmed cohorts
with corresponding controls were acquired, and metabolomics investigations were
performed using 1H NMR technique. Thereafter, computational optimizations were
conducted on all three high-dimensional datasets that were generated from the disease
studies outlined, so that key biomarkers and most e cient optimizers were identi ed
in each study. The clinical and biochemical signi cance of the results arising from
this work were discussed and highlighted
Substructural local search in discrete estimation of distribution algorithms
Tese dout., Engenharia Electrónica e Computação, Universidade do Algarve, 2009SFRH/BD/16980/2004The last decade has seen the rise and consolidation of a new trend of stochastic
optimizers known as estimation of distribution algorithms (EDAs). In essence, EDAs
build probabilistic models of promising solutions and sample from the corresponding
probability distributions to obtain new solutions. This approach has brought a new
view to evolutionary computation because, while solving a given problem with an
EDA, the user has access to a set of models that reveal probabilistic dependencies
between variables, an important source of information about the problem.
This dissertation proposes the integration of substructural local search (SLS)
in EDAs to speedup the convergence to optimal solutions. Substructural neighborhoods
are de ned by the structure of the probabilistic models used in EDAs,
generating adaptive neighborhoods capable of automatic discovery and exploitation
of problem regularities. Speci cally, the thesis focuses on the extended compact
genetic algorithm and the Bayesian optimization algorithm. The utility of SLS in
EDAs is investigated for a number of boundedly di cult problems with modularity,
overlapping, and hierarchy, while considering important aspects such as scaling
and noise. The results show that SLS can substantially reduce the number of function
evaluations required to solve some of these problems. More importantly, the
speedups obtained can scale up to the square root of the problem size O(
p
`).Fundação para a Ciência e Tecnologia (FCT
Personality Identification from Social Media Using Deep Learning: A Review
Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed