Machine Learning
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Feature selection for high-dimensional genomic microarray data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
Using One-Class and Two-Class SVMs for Multiclass Image Annotation
IEEE Transactions on Knowledge and Data Engineering
2005 Special Issue: Challenges in real-life emotion annotation and machine learning based detection
Neural Networks - Special issue: Emotion and brain
Expert Systems with Applications: An International Journal
Recognizing plankton images from the shadow image particle profiling evaluation recorder
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Evolutionary optimization of radial basis function classifiers for data mining applications
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Handling class imbalance in customer churn prediction
Expert Systems with Applications: An International Journal
FAC '09 Proceedings of the 5th International Conference on Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience: Held as Part of HCI International 2009
Application of a 3NN+1 based CBR system to segmentation of the notebook computers market
Expert Systems with Applications: An International Journal
Feature selection algorithm for ECG signals using Range-Overlaps Method
Expert Systems with Applications: An International Journal
Ensemble classification based on generalized additive models
Computational Statistics & Data Analysis
An instance-based schema matching method with attributes ranking and classification
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 5
Fundamenta Informaticae - Intelligent Data Analysis in Granular Computing
Expert Systems with Applications: An International Journal
Two-stage multinomial logit model
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction
Expert Systems with Applications: An International Journal
Data augmentation by predicting spending pleasure using commercially available external data
Journal of Intelligent Information Systems
Expert Systems with Applications: An International Journal
A new multi-task learning technique to predict classification of leukemia and prostate cancer
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Scalable subspace logistic regression models for high dimensional data
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Customer event history for churn prediction: How long is long enough?
Expert Systems with Applications: An International Journal
Kernel Factory: An ensemble of kernel machines
Expert Systems with Applications: An International Journal
Assessing similarity of feature selection techniques in high-dimensional domains
Pattern Recognition Letters
Hi-index | 12.06 |
Several supervised learning algorithms are suited to classify instances into a multiclass value space. MultiNomial Logit (MNL) is recognized as a robust classifier and is commonly applied within the CRM (Customer Relationship Management) domain. Unfortunately, to date, it is unable to handle huge feature spaces typical of CRM applications. Hence, the analyst is forced to immerse himself into feature selection. Surprisingly, in sharp contrast with binary logit, current software packages lack any feature-selection algorithm for MultiNomial Logit. Conversely, Random Forests, another algorithm learning multiclass problems, is just like MNL robust but unlike MNL it easily handles high-dimensional feature spaces. This paper investigates the potential of applying the Random Forests principles to the MNL framework. We propose the Random MultiNomial Logit (RMNL), i.e. a random forest of MNLs, and compare its predictive performance to that of (a) MNL with expert feature selection, (b) Random Forests of classification trees. We illustrate the Random MultiNomial Logit on a cross-sell CRM problem within the home-appliances industry. The results indicate a substantial increase in model accuracy of the RMNL model to that of the MNL model with expert feature selection.