Multi-instance genetic programming for web index recommendation

Dept. of Computer Sciences and Artificial Intelligence, University of Granada, Periodista Daniel Saucedo Aranda s/n, 18071 Granada, Spain
Expert Systems with Applications (Impact Factor: 2.24). 11/2009; 36(9):11470-11479. DOI: 10.1016/j.eswa.2009.03.059


This article introduces the use of a multi-instance genetic programming algorithm for modelling user preferences in web index recommendation systems. The developed algorithm learns user interest by means of rules which add comprehensibility and clarity to the discovered models and increase the quality of the recommendations. This new model, called G3P-MI algorithm, is evaluated and compared with other available algorithms. Computational experiments show that our methodology achieves competitive results and provide high-quality user models which improve the accuracy of recommendations.

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    • "4.1. Representation of web index recommendation data Like Zafra et al.'s frequency GP3MI and MOG3P-MI, [39] [40], we use a classical vector representation whose components are term frequencies, and apply the same feature selection preprocessing step. However, while their methods use absolute frequencies in the feature values, we calculate a weighted and normalized value. "
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    ABSTRACT: Web index recommendation systems are designed to help internet users with suggestions for finding relevant information. One way to develop such systems is using the multi-instance learning (MIL) approach: a generalization of the traditional supervised learning where each example is a labeled bag that is composed of unlabeled instances, and the task is to predict the labels of unseen bags. This paper proposes a multi-instance learning wrapper method using the Rocchio classifier to recommend web index pages. The wrapper implements a new way to relate the instances with the class labels of the bags. The proposed method has low computational cost and the experimental study on benchmark data sets shows that it performs better than the state-of-the-art methods for this problem.
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    • ") , web index page recommendation ( Zafra et al . , 2009b ) , semantic video retrieval ( Chen and Chen , 2009 ) , video concept detection ( Gu et al . , 2008 ; Gao and Sun , 2008 ) and pedestrian detection ( Pang et al . , 2008 ) . In all cases MIL provides a more natural form of representation that achieves better the results than those obtained by traditional supervised learning ."
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    ABSTRACT: In this paper, a new approach based on multiple instance learning is proposed to predict student’s performance and to improve the obtained results using a classical single instance learning. Multiple instance learning provides a more suitable and optimized representation that is adapted to available information of each student and course eliminating the missing values that make difficult to find efficient solutions when traditional supervised learning is used. To check the efficiency of the new proposed representation, the most popular techniques of traditional supervised learning based on single instances are compared to those based on multiple instance learning. Computational experiments show that when the problem is regarded as a multiple instance one, performance is significantly better and the weaknesses of single-instance representation are overcome.
    Full-text · Article · Nov 2011 · Expert Systems with Applications
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    • "Research on MIL has grown enormously in the last years due to the great number of applications, for which the problem formulation and representation as MIL is more appropriate than traditional supervised learning. Examples include approaches for text categorization [5], content-based image retrieval [6], [7] and image annotation [8], drug activity prediction [9], [10], web index page recommendation [11], semantic video retrieval [12], video concept detection [13] and prediction of student performance [14]. In all cases MIL provides a more natural form of representation that achieves to improve the results obtained by the traditional supervised learning. "
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    ABSTRACT: Dimensionality reduction and feature selection in particular are known to be of a great help for making supervised learning more effective and efficient. Many different feature selection techniques have been proposed for the traditional settings, where each instance is expected to have a label. In multiple instance learning (MIL) each example or bag consists of a variable set of instances, and the label is known for the bag as a whole, but not for the individual instances it consists of. Therefore, utilizing class labels for feature selection in MIL is not that straightforward and traditional approaches for feature selection are not directly applicable. This paper proposes a filter feature selection approach based on the ReliefF technique. It allows any previously designed MIL method to benefit from our feature selection approach, which helps to cope with the curse of dimensionality. Experimental results show the effectiveness of the proposed approach in MIL - different MIL algorithms tend to perform better when applied after the dimensionality reduction.
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