348,793 research outputs found
Structured Learning of Tree Potentials in CRF for Image Segmentation
We propose a new approach to image segmentation, which exploits the
advantages of both conditional random fields (CRFs) and decision trees. In the
literature, the potential functions of CRFs are mostly defined as a linear
combination of some pre-defined parametric models, and then methods like
structured support vector machines (SSVMs) are applied to learn those linear
coefficients. We instead formulate the unary and pairwise potentials as
nonparametric forests---ensembles of decision trees, and learn the ensemble
parameters and the trees in a unified optimization problem within the
large-margin framework. In this fashion, we easily achieve nonlinear learning
of potential functions on both unary and pairwise terms in CRFs. Moreover, we
learn class-wise decision trees for each object that appears in the image. Due
to the rich structure and flexibility of decision trees, our approach is
powerful in modelling complex data likelihoods and label relationships. The
resulting optimization problem is very challenging because it can have
exponentially many variables and constraints. We show that this challenging
optimization can be efficiently solved by combining a modified column
generation and cutting-planes techniques. Experimental results on both binary
(Graz-02, Weizmann horse, Oxford flower) and multi-class (MSRC-21, PASCAL VOC
2012) segmentation datasets demonstrate the power of the learned nonlinear
nonparametric potentials.Comment: 10 pages. Appearing in IEEE Transactions on Neural Networks and
Learning System
A Machine learning approach to POS tagging
We have applied inductive learning of statistical decision trees
and relaxation labelling to the Natural Language Processing (NLP)
task of morphosyntactic disambiguation (Part Of Speech Tagging).
The learning process is supervised and obtains a language
model oriented to resolve POS ambiguities. This model consists
of a set of statistical decision trees expressing distribution of
tags and words in some relevant contexts.
The acquired language models are complete enough to be directly
used as sets of POS disambiguation rules, and include more complex
contextual information than simple collections of n-grams usually
used in statistical taggers.
We have implemented a quite simple and fast tagger that has been
tested and evaluated on the Wall Street Journal (WSJ) corpus with
a remarkable accuracy.
However, better results can be obtained by translating the trees
into rules to feed a flexible relaxation labelling based tagger.
In this direction we describe a tagger which is able to use
information of any kind (n-grams, automatically acquired constraints,
linguistically motivated manually written constraints, etc.), and in
particular to incorporate the machine learned decision trees.
Simultaneously, we address the problem of tagging when only
small training material is available, which is crucial in any process
of constructing, from scratch, an annotated corpus. We show that quite
high accuracy can be achieved with our system in this situation.Postprint (published version
Interpretable Categorization of Heterogeneous Time Series Data
Understanding heterogeneous multivariate time series data is important in
many applications ranging from smart homes to aviation. Learning models of
heterogeneous multivariate time series that are also human-interpretable is
challenging and not adequately addressed by the existing literature. We propose
grammar-based decision trees (GBDTs) and an algorithm for learning them. GBDTs
extend decision trees with a grammar framework. Logical expressions derived
from a context-free grammar are used for branching in place of simple
thresholds on attributes. The added expressivity enables support for a wide
range of data types while retaining the interpretability of decision trees. In
particular, when a grammar based on temporal logic is used, we show that GBDTs
can be used for the interpretable classi cation of high-dimensional and
heterogeneous time series data. Furthermore, we show how GBDTs can also be used
for categorization, which is a combination of clustering and generating
interpretable explanations for each cluster. We apply GBDTs to analyze the
classic Australian Sign Language dataset as well as data on near mid-air
collisions (NMACs). The NMAC data comes from aircraft simulations used in the
development of the next-generation Airborne Collision Avoidance System (ACAS
X).Comment: 9 pages, 5 figures, 2 tables, SIAM International Conference on Data
Mining (SDM) 201
Formal Verification of Input-Output Mappings of Tree Ensembles
Recent advances in machine learning and artificial intelligence are now being
considered in safety-critical autonomous systems where software defects may
cause severe harm to humans and the environment. Design organizations in these
domains are currently unable to provide convincing arguments that their systems
are safe to operate when machine learning algorithms are used to implement
their software.
In this paper, we present an efficient method to extract equivalence classes
from decision trees and tree ensembles, and to formally verify that their
input-output mappings comply with requirements. The idea is that, given that
safety requirements can be traced to desirable properties on system
input-output patterns, we can use positive verification outcomes in safety
arguments. This paper presents the implementation of the method in the tool
VoTE (Verifier of Tree Ensembles), and evaluates its scalability on two case
studies presented in current literature.
We demonstrate that our method is practical for tree ensembles trained on
low-dimensional data with up to 25 decision trees and tree depths of up to 20.
Our work also studies the limitations of the method with high-dimensional data
and preliminarily investigates the trade-off between large number of trees and
time taken for verification
Power System Parameters Forecasting Using Hilbert-Huang Transform and Machine Learning
A novel hybrid data-driven approach is developed for forecasting power system
parameters with the goal of increasing the efficiency of short-term forecasting
studies for non-stationary time-series. The proposed approach is based on mode
decomposition and a feature analysis of initial retrospective data using the
Hilbert-Huang transform and machine learning algorithms. The random forests and
gradient boosting trees learning techniques were examined. The decision tree
techniques were used to rank the importance of variables employed in the
forecasting models. The Mean Decrease Gini index is employed as an impurity
function. The resulting hybrid forecasting models employ the radial basis
function neural network and support vector regression. Apart from introduction
and references the paper is organized as follows. The section 2 presents the
background and the review of several approaches for short-term forecasting of
power system parameters. In the third section a hybrid machine learning-based
algorithm using Hilbert-Huang transform is developed for short-term forecasting
of power system parameters. Fourth section describes the decision tree learning
algorithms used for the issue of variables importance. Finally in section six
the experimental results in the following electric power problems are
presented: active power flow forecasting, electricity price forecasting and for
the wind speed and direction forecasting
Direct-to-Patient Survey for Diagnosis of Benign Paroxysmal Positional Vertigo
Given the high incidence of dizziness and its frequent misdiagnosis, we aim to create a clinical support system to classify the presence or absence of benign paroxysmal positional vertigo with high accuracy and specificity. This paper describes a three-phase study currently underway for classification of benign paroxysmal positional vertigo, which includes diagnosis by a specialist in a clinical setting. Patient background information is collected by a survey on an Android tablet and machine learning techniques are applied for classification. Decision trees and wrappers are employed for their ability to provide information about the question set. One goal of the study is to attain an optimal question set. Each phase of the study presents a unique set and style of questions. Results achieved in the first two phases of the survey indicate that our approach using decision trees with filters or wrappers does a good job of identifying benign paroxysmal positional vertigo
GENESIM : genetic extraction of a single, interpretable model
Models obtained by decision tree induction techniques excel in being
interpretable.However, they can be prone to overfitting, which results in a low
predictive performance. Ensemble techniques are able to achieve a higher
accuracy. However, this comes at a cost of losing interpretability of the
resulting model. This makes ensemble techniques impractical in applications
where decision support, instead of decision making, is crucial.
To bridge this gap, we present the GENESIM algorithm that transforms an
ensemble of decision trees to a single decision tree with an enhanced
predictive performance by using a genetic algorithm. We compared GENESIM to
prevalent decision tree induction and ensemble techniques using twelve publicly
available data sets. The results show that GENESIM achieves a better predictive
performance on most of these data sets than decision tree induction techniques
and a predictive performance in the same order of magnitude as the ensemble
techniques. Moreover, the resulting model of GENESIM has a very low complexity,
making it very interpretable, in contrast to ensemble techniques.Comment: Presented at NIPS 2016 Workshop on Interpretable Machine Learning in
Complex System
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