12,727 research outputs found
Maximizing benefit of classifications using feature intervals
There is a great need for classification methods that can properly handle asymmetric cost and benefit constraints of classifications. In this study, we aim to emphasize the importance of classification benefits by means of a new classification algorithm, Benefit-Maximizing classifier with Feature Intervals (BMFI) that uses feature projection based knowledge representation. Empirical results show that BMFI has promising performance compared to recent cost-sensitive algorithms in terms of the benefit gained
Road Friction Estimation for Connected Vehicles using Supervised Machine Learning
In this paper, the problem of road friction prediction from a fleet of
connected vehicles is investigated. A framework is proposed to predict the road
friction level using both historical friction data from the connected cars and
data from weather stations, and comparative results from different methods are
presented. The problem is formulated as a classification task where the
available data is used to train three machine learning models including
logistic regression, support vector machine, and neural networks to predict the
friction class (slippery or non-slippery) in the future for specific road
segments. In addition to the friction values, which are measured by moving
vehicles, additional parameters such as humidity, temperature, and rainfall are
used to obtain a set of descriptive feature vectors as input to the
classification methods. The proposed prediction models are evaluated for
different prediction horizons (0 to 120 minutes in the future) where the
evaluation shows that the neural networks method leads to more stable results
in different conditions.Comment: Published at IV 201
Diagnosis of gastric carcinoma by classification on feature projections
Cataloged from PDF version of article.A new classification algorithm, called benefit maximizing classifier on feature projections (BCFP), is developed and applied to the problem of diagnosis of gastric carcinoma. The domain contains records of patients with known diagnosis through gastroscopy results. Given a training set of such records, the BCFP classifier learns how to differentiate a new case in the domain. BCFP represents a concept in the form of feature projections on each feature dimension separately. Classification in the BCFP algorithm is based on a voting among the individual predictions made on each feature. In the gastric carcinoma domain, a lesion can be an indicator of one of nine different Levels of gastric carcinoma, from early to late stages. The benefit of correct classification of early levels is much more than that of late cases. Also, the costs of wrong classifications are not symmetric. In the training phase, the BCFP algorithm learns classification rules that maximize the benefit of classification. In the querying phase, using these rules, the BCFP algorithm tries to make a prediction maximizing the benefit. A genetic algorithm is applied to select the relevant features. The performance of the BCFP algorithm is evaluated in terms of accuracy and running time. The rules induced are verified by experts of the domain. (C) 2004 Elsevier B.V. All rights reserved
Learning recurrent representations for hierarchical behavior modeling
We propose a framework for detecting action patterns from motion sequences
and modeling the sensory-motor relationship of animals, using a generative
recurrent neural network. The network has a discriminative part (classifying
actions) and a generative part (predicting motion), whose recurrent cells are
laterally connected, allowing higher levels of the network to represent high
level phenomena. We test our framework on two types of data, fruit fly behavior
and online handwriting. Our results show that 1) taking advantage of unlabeled
sequences, by predicting future motion, significantly improves action detection
performance when training labels are scarce, 2) the network learns to represent
high level phenomena such as writer identity and fly gender, without
supervision, and 3) simulated motion trajectories, generated by treating motion
prediction as input to the network, look realistic and may be used to
qualitatively evaluate whether the model has learnt generative control rules
Big Universe, Big Data: Machine Learning and Image Analysis for Astronomy
Astrophysics and cosmology are rich with data. The advent of wide-area
digital cameras on large aperture telescopes has led to ever more ambitious
surveys of the sky. Data volumes of entire surveys a decade ago can now be
acquired in a single night and real-time analysis is often desired. Thus,
modern astronomy requires big data know-how, in particular it demands highly
efficient machine learning and image analysis algorithms. But scalability is
not the only challenge: Astronomy applications touch several current machine
learning research questions, such as learning from biased data and dealing with
label and measurement noise. We argue that this makes astronomy a great domain
for computer science research, as it pushes the boundaries of data analysis. In
the following, we will present this exciting application area for data
scientists. We will focus on exemplary results, discuss main challenges, and
highlight some recent methodological advancements in machine learning and image
analysis triggered by astronomical applications
Identification of Hazardous Rural Highway Locations
An effective procedure was determined for identifying hazardous rural highway locations based on accident statistics. Multiple indicators of accident experience that are necessary include the number of fatal accidents, the total number of accidents, the number of effective-property-damage-only accidents, and the accident rate. Critical levels of these four indicators should vary from state to state depending on the nature of the local safety improvement program as well as local traffic and roadway conditions and prevailing attitudes toward highway safety. Specific recommendations are given for use in Kentucky. Critical accident rates are established using quality control procedures.
To identify hazardous highway locations, it is necessary to distinguish between short highway segments (spots) and large segments (sections) and to further classify spots as intersection and non-intersection locations. Intersection spots should include a distance of 0.15 mile (0.24 km) along all approaches; non-intersection spots should be 0.3-mile (0.48-km), floating segments; and sections should be 3-mile (4.8-km), floating segments. Both spots and sections should be classified by highway type and location. The use of dual time intervals of 1 and 2 years for accumulating and evaluating accident statistics was found to be desirable
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