5,470 research outputs found
Multi-dimensional clustering in user profiling
User profiling has attracted an enormous number of technological methods and
applications. With the increasing amount of products and services, user profiling
has created opportunities to catch the attention of the user as well as achieving
high user satisfaction. To provide the user what she/he wants, when and how,
depends largely on understanding them. The user profile is the representation of
the user and holds the information about the user. These profiles are the
outcome of the user profiling.
Personalization is the adaptation of the services to meet the user’s needs and
expectations. Therefore, the knowledge about the user leads to a personalized
user experience. In user profiling applications the major challenge is to build and
handle user profiles. In the literature there are two main user profiling methods,
collaborative and the content-based. Apart from these traditional profiling
methods, a number of classification and clustering algorithms have been used
to classify user related information to create user profiles. However, the profiling,
achieved through these works, is lacking in terms of accuracy. This is because,
all information within the profile has the same influence during the profiling even
though some are irrelevant user information.
In this thesis, a primary aim is to provide an insight into the concept of user
profiling. For this purpose a comprehensive background study of the literature
was conducted and summarized in this thesis. Furthermore, existing user
profiling methods as well as the classification and clustering algorithms were investigated. Being one of the objectives of this study, the use of these
algorithms for user profiling was examined. A number of classification and
clustering algorithms, such as Bayesian Networks (BN) and Decision Trees
(DTs) have been simulated using user profiles and their classification accuracy
performances were evaluated. Additionally, a novel clustering algorithm for the
user profiling, namely Multi-Dimensional Clustering (MDC), has been proposed.
The MDC is a modified version of the Instance Based Learner (IBL) algorithm.
In IBL every feature has an equal effect on the classification regardless of their
relevance. MDC differs from the IBL by assigning weights to feature values to
distinguish the effect of the features on clustering. Existing feature weighing
methods, for instance Cross Category Feature (CCF), has also been
investigated. In this thesis, three feature value weighting methods have been
proposed for the MDC. These methods are; MDC weight method by Cross
Clustering (MDC-CC), MDC weight method by Balanced Clustering (MDC-BC)
and MDC weight method by changing the Lower-limit to Zero (MDC-LZ). All of
these weighted MDC algorithms have been tested and evaluated. Additional
simulations were carried out with existing weighted and non-weighted IBL
algorithms (i.e. K-Star and Locally Weighted Learning (LWL)) in order to
demonstrate the performance of the proposed methods. Furthermore, a real life scenario is implemented to show how the MDC can be used for the user
profiling to improve personalized service provisioning in mobile environments.
The experiments presented in this thesis were conducted by using user profile
datasets that reflect the user’s personal information, preferences and interests.
The simulations with existing classification and clustering algorithms (e.g. Bayesian Networks (BN), Naïve Bayesian (NB), Lazy learning of Bayesian
Rules (LBR), Iterative Dichotomister 3 (Id3)) were performed on the WEKA
(version 3.5.7) machine learning platform. WEKA serves as a workbench to
work with a collection of popular learning schemes implemented in JAVA. In
addition, the MDC-CC, MDC-BC and MDC-LZ have been implemented on
NetBeans IDE 6.1 Beta as a JAVA application and MATLAB. Finally, the real life
scenario is implemented as a Java Mobile Application (Java ME) on NetBeans
IDE 7.1. All simulation results were evaluated based on the error rate and
accuracy
Parameter Optimization for Image Denoising Based on Block Matching and 3D Collaborative Filtering
Clinical MRI images are generally corrupted by random noise during acquisition with blurred subtle structure features. Many denoising methods have been proposed to remove noise from corrupted images at the expense of distorted structure features. Therefore, there is always compromise between removing noise and preserving structure information for denoising methods. For a specific denoising method, it is crucial to tune it so that the best tradeoff can be obtained. In this paper, we define several cost functions to assess the quality of noise removal and that of structure information preserved in the denoised image. Strength Pareto Evolutionary Algorithm 2 (SPEA2) is utilized to simultaneously optimize the cost functions by modifying parameters associated with the denoising methods. The effectiveness of the algorithm is demonstrated by applying the proposed optimization procedure to enhance the image denoising results using block matching and 3D collaborative filtering. Experimental results show that the proposed optimization algorithm can significantly improve the performance of image denoising methods in terms of noise removal and structure information preservation
An agent-driven semantical identifier using radial basis neural networks and reinforcement learning
Due to the huge availability of documents in digital form, and the deception
possibility raise bound to the essence of digital documents and the way they
are spread, the authorship attribution problem has constantly increased its
relevance. Nowadays, authorship attribution,for both information retrieval and
analysis, has gained great importance in the context of security, trust and
copyright preservation. This work proposes an innovative multi-agent driven
machine learning technique that has been developed for authorship attribution.
By means of a preprocessing for word-grouping and time-period related analysis
of the common lexicon, we determine a bias reference level for the recurrence
frequency of the words within analysed texts, and then train a Radial Basis
Neural Networks (RBPNN)-based classifier to identify the correct author. The
main advantage of the proposed approach lies in the generality of the semantic
analysis, which can be applied to different contexts and lexical domains,
without requiring any modification. Moreover, the proposed system is able to
incorporate an external input, meant to tune the classifier, and then
self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli
Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201
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