110 research outputs found
Regression on feature projections
Cataloged from PDF version of article.This paper describes a machine learning method, called Regression on Feature Projections (RFP), for predicting a real-valued target
feature, given the values of multiple predictive features. In RFP training is based on simply storing the projections of the training instances on
each feature separately. Prediction of the target value for a query point is obtained through two averaging procedures executed sequentially.
The ®rst averaging process is to ®nd the individual predictions of features by using the K-Nearest Neighbor (KNN) algorithm. The second
averaging process combines the predictions of all features. During the ®rst averaging step, each feature is associated with a weight in order to
determine the prediction ability of the feature at the local query point. The weights, found for each local query point, are used in the second
prediction step and enforce the method to have an adaptive or context-sensitive nature. We have compared RFP with KNN and the rule
based-regression algorithms. Results on real data sets show that RFP achieves better or comparable accuracy and is faster than both KNN and
Rule-based regression algorithms. (C)2000 Elsevier Science B.V. All rights reserved
Rule-based Machine Learning Methods for Functional Prediction
We describe a machine learning method for predicting the value of a
real-valued function, given the values of multiple input variables. The method
induces solutions from samples in the form of ordered disjunctive normal form
(DNF) decision rules. A central objective of the method and representation is
the induction of compact, easily interpretable solutions. This rule-based
decision model can be extended to search efficiently for similar cases prior to
approximating function values. Experimental results on real-world data
demonstrate that the new techniques are competitive with existing machine
learning and statistical methods and can sometimes yield superior regression
performance.Comment: See http://www.jair.org/ for any accompanying file
A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the U.S. central plains
Droughts are normal climate episodes, yet they are among the most expensive natural disasters in the world. Knowledge about the timing, severity, and pattern of droughts on the landscape can be incorporated into effective planning and decisionmaking. In this study, we present a data mining approach to modeling vegetation stress due to drought and mapping its spatial extent during the growing season. Rule-based regression tree models were generated that identify relationships between satellite-derived vegetation conditions, climatic drought indices, and biophysical data, including land-cover type, available soil water capacity, percent of irrigated farm land, and ecological type. The data mining method builds numerical rule-based models that find relationships among the input variables. Because the models can be applied iteratively with input data from previous time periods, the method enables to provide predictions of vegetation conditions farther into the growing season based on earlier conditions. Visualizing the model outputs as mapped information (called VegPredict) provides a means to evaluate the model. We present prototype maps for the 2002 drought year for Nebraska and South Dakota and discuss potential uses for these maps
An overview of regression techniques for knowledge discovery
Predicting or learning numeric features is called regression in the statistical literature, and it is the subject of research in both machine learning and statistics. This paper reviews the important techniques and algorithms for regression developed by both communities. Regression is important for many applications, since lots of real life problems can be modeled as regression problems. The review includes Locally Weighted Regression (LWR), rule-based regression, Projection Pursuit Regression (PPR), instance-based regression, Multivariate Adaptive Regression Splines (MARS) and recursive partitioning regression methods that induce regression trees (CART, RETIS and M5)
Predicition of groundwater level on Grohovo landslide using ruled based regression
In order to contribute to understanding the effect
of atmospheric conditions on the groundwater
level fluctuations on Grohovo landslide, a machine
learning tool for induction of models in form of the
set of rules was applied on a dataset comprising
daily atmospheric and groundwater level data
measured in 2012. The atmospheric data
comprises of an average daily air temperature,
humidity, wind speed, pressure, total
evapotranspiration, and precipitations. For the
experiment independent variables i.e. atmospheric
data and present groundwater level were used to
model target variable i.e. predicted groundwater
level for 24 and 48 hours in advance.
The presented models give predictions 24 (first
model) and 48 (second model) hours in advance
for groundwater level fluctuations on Grohovo
landslide. The first model is consisted from seven,
and the second model from five rules. Both models
have very high correlation coefficients of 0.99 and
0.97, respectively. From the given models, it can
be concluded that the most influence on the
groundwater level fluctuations have sum of daily
precipitations and average daily air temperature.
The obtained models are intended for use in the
models for debris flow propagation on the Rječina
River as a part of an Early Warning System
Using geophysical surveys to test tracer-based storage estimates in headwater catchments
Acknowledgements The authors are grateful to Stian Bradford, Chris Gabrielli, and Julie Timms for practical and logistical assistance. The provision of transport by Iain Malcolm and Ross Glover of Marine Scotland Science was greatly appreciated. We also thank the European Research Council ERC (project GA 335910 VEWA) for funding through the VeWa project and the Leverhulme Trust for funding through PLATO (RPG-2014-016).Peer reviewedPostprin
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