62,031 research outputs found
What Works Better? A Study of Classifying Requirements
Classifying requirements into functional requirements (FR) and non-functional
ones (NFR) is an important task in requirements engineering. However, automated
classification of requirements written in natural language is not
straightforward, due to the variability of natural language and the absence of
a controlled vocabulary. This paper investigates how automated classification
of requirements into FR and NFR can be improved and how well several machine
learning approaches work in this context. We contribute an approach for
preprocessing requirements that standardizes and normalizes requirements before
applying classification algorithms. Further, we report on how well several
existing machine learning methods perform for automated classification of NFRs
into sub-categories such as usability, availability, or performance. Our study
is performed on 625 requirements provided by the OpenScience tera-PROMISE
repository. We found that our preprocessing improved the performance of an
existing classification method. We further found significant differences in the
performance of approaches such as Latent Dirichlet Allocation, Biterm Topic
Modeling, or Naive Bayes for the sub-classification of NFRs.Comment: 7 pages, the 25th IEEE International Conference on Requirements
Engineering (RE'17
Automated Classification of Airborne Laser Scanning Point Clouds
Making sense of the physical world has always been at the core of mapping. Up
until recently, this has always dependent on using the human eye. Using
airborne lasers, it has become possible to quickly "see" more of the world in
many more dimensions. The resulting enormous point clouds serve as data sources
for applications far beyond the original mapping purposes ranging from flooding
protection and forestry to threat mitigation. In order to process these large
quantities of data, novel methods are required. In this contribution, we
develop models to automatically classify ground cover and soil types. Using the
logic of machine learning, we critically review the advantages of supervised
and unsupervised methods. Focusing on decision trees, we improve accuracy by
including beam vector components and using a genetic algorithm. We find that
our approach delivers consistently high quality classifications, surpassing
classical methods
A Network Topology Approach to Bot Classification
Automated social agents, or bots, are increasingly becoming a problem on
social media platforms. There is a growing body of literature and multiple
tools to aid in the detection of such agents on online social networking
platforms. We propose that the social network topology of a user would be
sufficient to determine whether the user is a automated agent or a human. To
test this, we use a publicly available dataset containing users on Twitter
labelled as either automated social agent or human. Using an unsupervised
machine learning approach, we obtain a detection accuracy rate of 70%
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