C4.5: programs for machine learning
C4.5: programs for machine learning
Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms for Feature Selection and Weighting, A Review and Study
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Fast Branch & Bound Algorithms for Optimal Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Random subspace method for multivariate feature selection
Pattern Recognition Letters
Pattern Recognition Letters
Adaptive branch and bound algorithm for selecting optimal features
Pattern Recognition Letters
Nonlinear Dimension Reduction with Kernel Sliced Inverse Regression
IEEE Transactions on Knowledge and Data Engineering
Local-Learning-Based Feature Selection for High-Dimensional Data Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature extraction for novelty detection as applied to fault detection in machinery
Pattern Recognition Letters
A multi-manifold discriminant analysis method for image feature extraction
Pattern Recognition
Kernel discriminant transformation for image set-based face recognition
Pattern Recognition
Expert Systems with Applications: An International Journal
Robust Feature Selection for Microarray Data Based on Multicriterion Fusion
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Wrapper–Filter Feature Selection Algorithm Using a Memetic Framework
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The use of dimension reduction techniques has attracted considerable attention owing to information explosion. Without considering the underlying phenomena of interest, traditional dimension reduction approaches aim to search a feature set for optimizing performance. In recommending entertainment videos, beyond the successful recommendations, marketing strategy can be benefited from interpreting precise social context information accurately. Therefore, how to find an easy-to-explain feature set to achieve optimal prediction performance becomes an important issue. In this paper, we propose a three-phase feature synthesis approach to search heuristically optimal feature set within exponential easy-to-explain features. The first phase performs feature selection by screening low-informative features, the second phase shrinks the high-dependent feature subset, and the third phase enhances the dominated features. An implemented social recommendation system and the 11 months purchasing data from the largest commercial entertainment video Web shop in Taiwan are adopted to evaluate the effectiveness and efficiency of the proposed feature synthesis method in the experiments. The experimental results show that our approach can obtain the interpretable clustering results as well as improve the recommendation.