A comparative study of selected classification accuracy in user profiling

In recent years the used of personalization in service provisioning applications has been very popular. However, effective personalization cannot be achieved without accurate user profiles. A number of classification algorithms have been used to classify user related information to create accurate user profiles. In this study four different classification algorithms which are; naive Bayesian (NB), Bayesian Networks (BN), lazy learning of Bayesian rules (LBR) and instance-based learner (IB1) are compared using a set of user profile data. According to our simulation results NB and IB1 classifiers have the highest classification accuracy with the lowest error rate

Classification accuracy performance of Naïve Bayesian (NB), Bayesian Networks (BN), Lazy Learning of Bayesian Rules(LBR) and Instance-Based Learner (IB1) - comparative study

In recent years the used of personalization in service provisioning applications has been very popular. However, effective personalization cannot be achieved without accurate user profiles. A number of classification algorithms have been used to classify user related information to create accurate user profiles. In this study four different classification algorithms which are; naive Bayesian (NB), Bayesian networks (BN), lazy learning of Bayesian rules (LBR) and instance-based learner (IB1) are compared using a set of user profile data. According to our simulation results NB and IB1 classifiers have the highest classification accuracy with the lowest error rate. The obtained simulation results have been evaluated against the existing works of support vector machines (SVMs), decision trees (DTs) and neural networks (NNs)

A Pairwise Naïve Bayes Approach to Bayesian Classification

Despite the relatively high accuracy of the naïve Bayes (NB) classifier, there may be several instances where it is not optimal, i.e. does not have the same classification performance as the Bayes classifier utilizing the joint distribution of the examined attributes. However, the Bayes classifier can be computationally intractable due to its required knowledge of the joint distribution. Therefore, we introduce a “pairwise naïve” Bayes (PNB) classifier that incorporates all pairwise relationships among the examined attributes, but does not require specification of the joint distribution. In this paper, we first describe the necessary and sufficient conditions under which the PNB classifier is optimal. We then discuss sufficient conditions for which the PNB classifier, and not NB, is optimal for normal attributes. Through simulation and actual studies, we evaluate the performance of our proposed classifier relative to the Bayes and NB classifiers, along with the HNB, AODE, LBR and TAN classifiers, using normal density and empirical estimation methods. Our applications show that the PNB classifier using normal density estimation yields the highest accuracy for data sets containing continuous attributes. We conclude that it offers a useful compromise between the Bayes and NB classifiers

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

Dynamic optimization of service part inventory control policy through applied data mining and simulation.

This research defines a novel approach for associating inventory item behavior, focusing initially on demand patterns, with an optimal inventory control policy. This method relies upon the definition of typical service part inventory demand patterns and the ability of data mining algorithms to classify inventory transaction data into one of these defined demand patterns. To facilitate this data mining effort, a simulation which creates archetypal inventory demand time series is proposed as the training data source for the data mining task. Actual service part inventory transactions thus classified will be used in a separate service part inventory simulation, modeling a multi-item inventory controlled using a set of common stochastic inventory control policies. Through simulation optimization, using simultaneous perturbation stochastic approximation (SPSA), an optimal demand-pattern to control-policy pairing is sought. The resulting set of optimal pairings will then be used to determine the optimal policy which should be applied to actual service part inventory items after performing demand classification data mining of the actual inventory transaction time series. Improving the efficiency of inventory management within the maintenance and repair service business area holds great promise for reducing inventory investment and improving customer service. Ideally, application of this research could enable an inventory management system which supports the use of multiple concurrent and dynamic inventory management policies focused on reducing inventory cost and increasing customer service and complex equipment availability

Novel Hierarchical Feature Selection Methods for Classification and Their Application to Datasets of Ageing-Related Genes

Hierarchical Feature Selection (HFS) is an under-explored subarea of data mining/machine learning. Unlike conventional (flat) feature selection algorithms, HFS algorithms work by exploiting hierarchical (generalisation-specialisation) relationships between features, in order to try to improve the predictive accuracy of classifiers. The basic idea is to remove hierarchical redundancy between features, where the presence of a feature in an instance implies the presence of all ancestors of that feature in that instance. By using an HFS algorithm to select a feature subset where the hierarchical redundancy among features is eliminated or reduced, and then giving only the selected feature subset to a classification algorithm, it is possible to improve the predictive accuracy of classification algorithms. In terms of applications, this thesis focuses on datasets of ageing-related genes. This type of dataset is an interesting type of application for data mining methods due to the technical difficulty and ethical issues associated with doing ageing experiments with humans and the strategic importance of research on the biology of ageing - since age is the greatest risk factor for a number of diseases, but is still a not well understood biological process. This thesis offers contributions mainly to the area of data mining/machine learning, but also to bioinformatics and the biology of ageing, as discussed next. The first and main type of contribution consists of four novel HFS algorithms, namely: select Hierarchical Information Preserving (HIP) features, select Most Relevant (MR) features, the hybrid HIP–MR algorithm, and the Hierarchy-based Redundancy Eliminated Tree Augmented Naive Bayes (HRE–TAN) algorithm. These algorithms perform lazy learning-based feature selection - i.e. they postpone the learning process to the moment when testing instances are observed and select a specific feature subset for each testing instance. HIP, MR and HIP–MR select features in a data pre-processing phase, before running a classification algorithm, and they select features that can be used as input by any lazy classification algorithm. In contrast, HRE–TAN is a feature selection process embedded in the construction of a lazy TAN classifier. The second type of contribution, relevant to the areas of data mining and bioinformatics, consists of two novel algorithms that exploit the pre-defined structure of the Gene Ontology (GO) and the results of a flat or hierarchical feature selection algorithm to create the network topology of a Bayesian Network Augmented Naive Bayes (BAN) classifier. These are called GO–BAN algorithms. The proposed HFS algorithms were in general evaluated in combination with lazy versions of three Bayesian network classifiers, namely Naïve Bayes, TAN and GO–BAN - except that HRE–TAN works only with TAN. The experiments involved comparing the predictive accuracy obtained by these classifiers using the features selected by the proposed HFS algorithms with the predictive accuracy obtained by these classifiers using the features selected by flat feature selection algorithms, as well as the accuracy obtained by the classifiers using all original features (without feature selection) as a baseline. The experiments used a number of ageing-related datasets, where the instances being classified are genes, the predictive features are GO terms describing hierarchical gene functions, and the classes to be predicted indicate whether a gene has a pro-longevity or anti-longevity effect in the lifespan of a model organism (yeast, worm, fly or mouse). In general, with the exception of the hybrid HIP–MR which did not obtain good results, the other three proposed HFS algorithms (HIP, MR, HRE–TAN) improved the predictive performance of the baseline Bayesian network classifiers - i.e. in general the classifiers obtained higher accuracies when using only the features selected by the HFS algorithm than when using all original features. Overall, the most successful of the four HFS algorithms was HIP, which outperformed all other (hierarchical or flat) feature selection algorithms when used in combination with each of the Naive Bayes, TAN and GO–BAN classifiers. The difference of predictive accuracy between HIP and the other feature selection algorithms was almost always statistically significant - except that the difference of accuracy between HIP and MR was not significant with TAN. Comparing different combinations of a HFS algorithm and a Bayesian network classifier, HIP+NB and HIP+GO–BAN were both the best combination, with the same average rank across all datasets. They obtained predictive accuracies statistically significantly higher than the accuracies obtained by all other combinations of HFS algorithm and classifier. The third type of contribution of this thesis is a contribution to the biology of ageing. More precisely, the proposed HIP and MR algorithms were used to produce rankings of GO terms in decreasing order of their usefulness for predicting the pro-longevity or anti-longevity effect of a gene on a model organism; and the top GO terms in these rankings were interpreted with the help of a biologist expert on ageing, leading to potentially relevant patterns about the biology of ageing