Tuve Löfström

发表

On the Use of Accuracy and Diversity Measures for Evaluating and Selecting Ensembles of Classifiers

Henrik Boström, Tuve Löfström, Ulf Johansson, 2008, 2008 Seventh International Conference on Machine Learning and Applications.

Classification with Reject Option Using Conformal Prediction

Henrik Boström, Tuve Löfström, Ulf Johansson, 2018, PAKDD.

Using Imaginary Ensembles to Select GP Classifiers

Lars Niklasson, Tuve Löfström, Ulf Johansson, 2010, EuroGP.

Rule Extraction from Opaque Models-- A Slightly Different Perspective

Lars Niklasson, Tuve Löfström, Ulf Johansson, 2006, 2006 5th International Conference on Machine Learning and Applications (ICMLA'06).

Random brains

Henrik Boström, Tuve Löfström, Ulf Johansson, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Utilizing Diversity and Performance Measures for Ensemble Creation

Tuve Löfström, Tuwe Löfström, 2009 .

Post-processing Evolved Decision Trees

Lars Niklasson, Tuve Löfström, Ulf Johansson, 2009, Foundations of Computational Intelligence.

Predicting Adverse Drug Events with Confidence

Jing Zhao, Tuve Löfström, Henrik Linusson, 2015, SCAI.

Benefits of relating the Retail Domain and Information Fusion

Tom Ziemke, Lars Niklasson, Tuve Löfström, 2006, 2006 9th International Conference on Information Fusion.

Improving GP classification performance by injection of decision trees

Lars Niklasson, Tuve Löfström, Ulf Johansson, 2010, IEEE Congress on Evolutionary Computation.

Using Conformal Prediction to Prioritize Compound Synthesis in Drug Discovery

Lars Carlsson, Henrik Boström, Tuve Löfström, 2017, COPA.

Accelerating difficulty estimation for conformal regression forests

Henrik Boström, Tuve Löfström, Ulf Johansson, 2017, Annals of Mathematics and Artificial Intelligence.

Genetically Evolved Trees Representing Ensembles

Lars Niklasson, Tuve Löfström, Ulf Johansson, 2006, ICAISC.

Proceedings of SAIS 2007 : The 24th Annual Workshop of the Swedish Artificial Intelligence Society, Borås, May 22-23, 2007

Lars Niklasson, Tuve Löfström, Rikard König, 2007 .

Regression conformal prediction with random forests

Henrik Boström, Tuve Löfström, Ulf Johansson, 2014, Machine Learning.

Using genetic programming to obtain implicit diversity

Tuve Löfström, Ulf Johansson, Rikard König, 2009, 2009 IEEE Congress on Evolutionary Computation.

Interpretable regression trees using conformal prediction

Henrik Boström, Tuve Löfström, Ulf Johansson, 2018, Expert Syst. Appl..

Reliable Confidence Predictions Using Conformal Prediction

Henrik Boström, Tuve Löfström, Ulf Johansson, 2016, PAKDD.

Effective utilization of data in inductive conformal prediction using ensembles of neural networks

Henrik Boström, Tuve Löfström, Ulf Johansson, 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

Evaluation of a Variance-Based Nonconformity Measure for Regression Forests

Henrik Boström, Tuve Löfström, Ulf Johansson, 2016, COPA.

Why Not Use an Oracle When You Got One

Lars Niklasson, Tuve Löfström, Ulf Johansson, 2006 .

Conformal Prediction Using Decision Trees

Henrik Boström, Tuve Löfström, Ulf Johansson, 2013, 2013 IEEE 13th International Conference on Data Mining.

Efficient Venn predictors using random forests

Henrik Boström, Tuve Löfström, Ulf Johansson, 2018, Machine Learning.

Obtaining Accurate Neural Network Ensembles

Lars Niklasson, Tuve Löfström, Ulf Johansson, 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

Increasing rule extraction accuracy by post-processing GP trees

Lars Niklasson, Tuve Löfström, Ulf Johansson, 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

The INFUSIS Project: Data and Text Mining for In Silico Modeling

Henrik Boström, Sorel Muresan, Tuve Löfström, 2010 .

Producing implicit diversity in ANN ensembles

Tuve Löfström, Ulf Johansson, U. Johansson, 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).

Using Feature Selection with Bagging and Rule Extraction in Drug Discovery

Henrik Boström, Tuve Löfström, Ulf Johansson, 2010 .

Introducing GEMS - A Novel Technique for Ensemble Creation

Lars Niklasson, Tuve Löfström, Ulf Johansson, 2006, FLAIRS Conference.

Overproduce-and-select: The grim reality

Henrik Boström, Tuve Löfström, Ulf Johansson, 2013, 2013 IEEE Symposium on Computational Intelligence and Ensemble Learning (CIEL).

Evaluating Ensembles on QSAR Classification

Tuve Löfström, Ulf Johansson, Ulf Norinder, 2009 .

Accurate Neural Network Ensembles Using Genetic Programming

Lars Niklasson, Tuve Löfström, Ulf Johansson, 2006 .

Oracle Coached Decision Trees and Lists

Tuve Löfström, Ulf Johansson, Cecilia Sönströd, 2010, IDA.

Rule Extraction with Guaranteed Fidelity

Henrik Boström, Tuve Löfström, Ulf Johansson, 2014, AIAI Workshops.

Signed-Error Conformal Regression

Tuve Löfström, Ulf Johansson, Henrik Linusson, 2014, PAKDD.

Bias reduction through conditional conformal prediction

Henrik Boström, Tuve Löfström, Ulf Johansson, 2015, Intell. Data Anal..

Efficiency Comparison of Unstable Transductive and Inductive Conformal Classifiers

Henrik Boström, Tuve Löfström, Ulf Johansson, 2014, AIAI Workshops.

On the Calibration of Aggregated Conformal Predictors

Henrik Boström, Tuve Löfström, Ulf Johansson, 2017, COPA.

System Health Monitoring using Conformal Anomaly Detection

Tuve Löfström, Ulf Johansson, Cecilia Sönströd, 2015 .

Empirically investigating the importance of diversity

Lars Niklasson, Tuve Löfström, Ulf Johansson, 2007, 2007 10th International Conference on Information Fusion.

Ensemble member selection using multi-objective optimization

Henrik Boström, Tuve Löfström, Ulf Johansson, 2009, 2009 IEEE Symposium on Computational Intelligence and Data Mining.

Using Optimized Optimization Criteria in Ensemble Member Selection

Henrik Boström, Tuve Löfström, Ulf Johansson, 2009 .

Model-agnostic nonconformity functions for conformal classification

Henrik Boström, Tuve Löfström, Ulf Johansson, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

The Importance of Diversity in Neural Network Ensembles - An Empirical Investigation

Lars Niklasson, Tuve Löfström, Ulf Johansson, 2007, 2007 International Joint Conference on Neural Networks.

Predicting the Benefit of Rule Extraction: A Novel Component in Data Mining

Tuve Löfström, Ulf Johansson, 2005 .

One tree to explain them all

Tuve Löfström, Ulf Johansson, Cecilia Sönströd, 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

Evaluating Standard Techniques for Implicit Diversity

Lars Niklasson, Tuve Löfström, Ulf Johansson, 2008, PAKDD.

Accuracy on a Hold-out Set: The Red Herring of Data Mining

Lars Niklasson, Tuve Löfström, Ulf Johansson, 2006 .

Building Neural Network Ensembles using Genetic Programming

Lars Niklasson, Tuve Löfström, Ulf Johansson, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Evolved decision trees as conformal predictors

Henrik Boström, Tuve Löfström, Ulf Johansson, 2013, 2013 IEEE Congress on Evolutionary Computation.

Obtaining accurate and comprehensible classifiers using oracle coaching

Henrik Boström, Tuve Löfström, Ulf Johansson, 2012, Intell. Data Anal..

Chipper - A Novel Algorithm for Concept Description

Henrik Boström, Tuve Löfström, Ulf Johansson, 2008, SCAI.

Evaluating Algorithms for Concept Description

Tuve Löfström, Ulf Johansson, Cecilia Sönströd, 2009, DMIN.

Locally induced predictive models

Tuve Löfström, Ulf Johansson, Cecilia Sönströd, 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

Rule Extraction by Seeing Through the Model

Lars Niklasson, Tuve Löfström, Ulf Johansson, 2004, ICONIP.

The problem with ranking ensembles based on training or validation performance

Henrik Boström, Tuve Löfström, Ulf Johansson, 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).