7,727 research outputs found
J-model: an open and social ensemble learning architecture for classification
Ensemble learning is a promising direction of research in machine learning, in which an ensemble
classifier gives better predictive and more robust performance for classification problems
by combining other learners. Meanwhile agent-based systems provide frameworks to
share knowledge from multiple agents in an open context. This thesis combines multi-agent
knowledge sharing with ensemble methods to produce a new style of learning system for open
environments.
We now are surrounded by many smart objects such as wireless sensors, ambient communication
devices, mobile medical devices and even information supplied via other humans. When
we coordinate smart objects properly, we can produce a form of collective intelligence from
their collaboration. Traditional ensemble methods and agent-based systems have complementary
advantages and disadvantages in this context. Traditional ensemble methods show better
classification performance, while agent-based systems might not guarantee their performance
for classification. Traditional ensemble methods work as closed and centralised systems
(so they cannot handle classifiers in an open context), while agent-based systems are natural
vehicles for classifiers in an open context.
We designed an open and social ensemble learning architecture, named J-model, to merge the
conflicting benefits of the two research domains. The J-model architecture is based on a service
choreography approach for coordinating classifiers. Coordination protocols are defined by
interaction models that describe how classifiers will interact with one another in a peer-to-peer
manner. The peer ranking algorithm recommends more appropriate classifiers to participate in
an interaction model to boost the success rate of results of their interactions. Coordinated participant
classifiers who are recommended by the peer ranking algorithm become an ensemble
classifier within J-model.
We evaluated J-modelâs classification performance with 13 UCI machine learning benchmark
data sets and a virtual screening problem as a realistic classification problem. J-model showed
better performance of accuracy, for 9 benchmark sets out of 13 data sets, than 8 other representative
traditional ensemble methods. J-model gave better results of specificity for 7 benchmark
sets. In the virtual screening problem, J-model gave better results for 12 out of 16 bioassays
than already published results. We defined different interaction models for each specific classification
task and the peer ranking algorithm was used across all the interaction models.
Our research contributions to knowledge are as follows. First, we showed that service choreography
can be an effective ensemble coordination method for classifiers in an open context. Second, we used interaction models that implement task specific coordinations of classifiers to
solve a variety of representative classification problems. Third, we designed the peer ranking
algorithm which is generally and independently applicable to the task of recommending appropriate
member classifiers from a classifier pool based on an open pool of interaction models
and classifiers
MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense
Present attack methods can make state-of-the-art classification systems based
on deep neural networks misclassify every adversarially modified test example.
The design of general defense strategies against a wide range of such attacks
still remains a challenging problem. In this paper, we draw inspiration from
the fields of cybersecurity and multi-agent systems and propose to leverage the
concept of Moving Target Defense (MTD) in designing a meta-defense for
'boosting' the robustness of an ensemble of deep neural networks (DNNs) for
visual classification tasks against such adversarial attacks. To classify an
input image, a trained network is picked randomly from this set of networks by
formulating the interaction between a Defender (who hosts the classification
networks) and their (Legitimate and Malicious) users as a Bayesian Stackelberg
Game (BSG). We empirically show that this approach, MTDeep, reduces
misclassification on perturbed images in various datasets such as MNIST,
FashionMNIST, and ImageNet while maintaining high classification accuracy on
legitimate test images. We then demonstrate that our framework, being the first
meta-defense technique, can be used in conjunction with any existing defense
mechanism to provide more resilience against adversarial attacks that can be
afforded by these defense mechanisms. Lastly, to quantify the increase in
robustness of an ensemble-based classification system when we use MTDeep, we
analyze the properties of a set of DNNs and introduce the concept of
differential immunity that formalizes the notion of attack transferability.Comment: Accepted to the Conference on Decision and Game Theory for Security
(GameSec), 201
Consistency in Models for Distributed Learning under Communication Constraints
Motivated by sensor networks and other distributed settings, several models
for distributed learning are presented. The models differ from classical works
in statistical pattern recognition by allocating observations of an independent
and identically distributed (i.i.d.) sampling process amongst members of a
network of simple learning agents. The agents are limited in their ability to
communicate to a central fusion center and thus, the amount of information
available for use in classification or regression is constrained. For several
basic communication models in both the binary classification and regression
frameworks, we question the existence of agent decision rules and fusion rules
that result in a universally consistent ensemble. The answers to this question
present new issues to consider with regard to universal consistency. Insofar as
these models present a useful picture of distributed scenarios, this paper
addresses the issue of whether or not the guarantees provided by Stone's
Theorem in centralized environments hold in distributed settings.Comment: To appear in the IEEE Transactions on Information Theor
Hi, how can I help you?: Automating enterprise IT support help desks
Question answering is one of the primary challenges of natural language
understanding. In realizing such a system, providing complex long answers to
questions is a challenging task as opposed to factoid answering as the former
needs context disambiguation. The different methods explored in the literature
can be broadly classified into three categories namely: 1) classification
based, 2) knowledge graph based and 3) retrieval based. Individually, none of
them address the need of an enterprise wide assistance system for an IT support
and maintenance domain. In this domain the variance of answers is large ranging
from factoid to structured operating procedures; the knowledge is present
across heterogeneous data sources like application specific documentation,
ticket management systems and any single technique for a general purpose
assistance is unable to scale for such a landscape. To address this, we have
built a cognitive platform with capabilities adopted for this domain. Further,
we have built a general purpose question answering system leveraging the
platform that can be instantiated for multiple products, technologies in the
support domain. The system uses a novel hybrid answering model that
orchestrates across a deep learning classifier, a knowledge graph based context
disambiguation module and a sophisticated bag-of-words search system. This
orchestration performs context switching for a provided question and also does
a smooth hand-off of the question to a human expert if none of the automated
techniques can provide a confident answer. This system has been deployed across
675 internal enterprise IT support and maintenance projects.Comment: To appear in IAAI 201
Cognitive system to achieve human-level accuracy in automated assignment of helpdesk email tickets
Ticket assignment/dispatch is a crucial part of service delivery business
with lot of scope for automation and optimization. In this paper, we present an
end-to-end automated helpdesk email ticket assignment system, which is also
offered as a service. The objective of the system is to determine the nature of
the problem mentioned in an incoming email ticket and then automatically
dispatch it to an appropriate resolver group (or team) for resolution.
The proposed system uses an ensemble classifier augmented with a configurable
rule engine. While design of classifier that is accurate is one of the main
challenges, we also need to address the need of designing a system that is
robust and adaptive to changing business needs. We discuss some of the main
design challenges associated with email ticket assignment automation and how we
solve them. The design decisions for our system are driven by high accuracy,
coverage, business continuity, scalability and optimal usage of computational
resources.
Our system has been deployed in production of three major service providers
and currently assigning over 40,000 emails per month, on an average, with an
accuracy close to 90% and covering at least 90% of email tickets. This
translates to achieving human-level accuracy and results in a net saving of
about 23000 man-hours of effort per annum
- âŠ