Coarse-grained Classification of Web Sites by Their Structural Properties

In this paper, we identify and analyze structural properties which reflect the functionality of a Web site. These structural properties consider the size, the organization, the composition of URLs, and the link structure of Web sites. Opposed to previous work, we perform a comprehensive measurement study to delve into the relation between the structure and the functionality of Web sites. Our study focuses on five of the most relevant functional classes, namely Academic, Blog, Corporate, Personal, and Shop. It is based upon more than 1,400 Web sites composed of 7 million crawled and 47 million known Web pages. We present a detailed statistical analysis which provides insight into how structural properties can be used to distinguish between Web sites from different functional classes. Building on these results, we introduce a content-independent approach for the automated coarse-grained classification of Web sites. A naïve Bayesian classifier with advanced density estimation yields a precision of 82% and recall of 80% for the classification of Web sites into the considered classes

Weighted Proportional k-Interval Discretization for Naive-Bayes Classifiers

Abstract. The use of different discretization techniques can be expected to affect the classification bias and variance of naive-Bayes classifiers. We call such an effect discretization bias and variance. Proportional kinterval discretization (PKID) tunes discretization bias and variance by adjusting discretized interval size and number proportional to the number of training instances. Theoretical analysis suggests that this is desirable for naive-Bayes classifiers. However PKID is sub-optimal when learning from training data of small size. We argue that this is because PKID equally weighs bias reduction and variance reduction. But for small data, variance reduction can contribute more to lower learning error and thus should be given greater weight than bias reduction. Accordingly we propose weighted proportional k-interval discretization (WPKID), which establishes a more suitable bias and variance trade-off for small data while allowing additional training data to be used to reduce both bias and variance. Our experiments demonstrate that for naive-Bayes classifiers, WPKID improves upon PKID for smaller datasets 1 with significant frequency; and WPKID delivers lower classification error significantly more often than not in comparison to three other leading alternative discretization techniques studied.

Longitudinal study of first-time freshmen using data mining

In the modern world, higher education is transitioning from enrollment mode to recruitment mode. This shift paved the way for institutional research and policy making from historical data perspective. More and more universities in the U.S. are implementing and using enterprise resource planning (ERP) systems, which collect vast amounts of data. Although few researchers have used data mining for performance, graduation rates, and persistence prediction, research is sparse in this area, and it lacks the rigorous development and evaluation of data mining models. The primary objective of this research was to build and analyze data mining models using historical data to find out patterns and rules that classified students who were likely to drop-out and students who were likely to persist.;Student retention is a major problem for higher education institutions, and predictive models developed using traditional quantitative methods do not produce results with high accuracy, because of massive amounts of data, correlation between attributes, missing values, and non-linearity of variables; however, data mining techniques work well with these conditions. In this study, various data mining models were used along with discretization, feature subset selection, and cross-validation; the results were not only analyzed using the probability of detection and probability of false alarm, but were also analyzed using variances obtained in these performance measures. Attributes were grouped together based on the current hypotheses in the literature. Using the results of feature subset selectors and treatment learners, attributes that contributed the most toward a student\u27s decision of dropping out or staying were found, and specific rules were found that characterized a successful student. The performance measures obtained in this study were significantly better than previously reported in the literature

Predictive Modelling Approach to Data-Driven Computational Preventive Medicine

This thesis contributes novel predictive modelling approaches to data-driven computational preventive medicine and offers an alternative framework to statistical analysis in preventive medicine research. In the early parts of this research, this thesis presents research by proposing a synergy of machine learning methods for detecting patterns and developing inexpensive predictive models from healthcare data to classify the potential occurrence of adverse health events. In particular, the data-driven methodology is founded upon a heuristic-systematic assessment of several machine-learning methods, data preprocessing techniques, models’ training estimation and optimisation, and performance evaluation, yielding a novel computational data-driven framework, Octopus. Midway through this research, this thesis advances research in preventive medicine and data mining by proposing several new extensions in data preparation and preprocessing. It offers new recommendations for data quality assessment checks, a novel multimethod imputation (MMI) process for missing data mitigation, a novel imbalanced resampling approach, and minority pattern reconstruction (MPR) led by information theory. This thesis also extends the area of model performance evaluation with a novel classification performance ranking metric called XDistance. In particular, the experimental results show that building predictive models with the methods guided by our new framework (Octopus) yields domain experts' approval of the new reliable models’ performance. Also, performing the data quality checks and applying the MMI process led healthcare practitioners to outweigh predictive reliability over interpretability. The application of MPR and its hybrid resampling strategies led to better performances in line with experts' success criteria than the traditional imbalanced data resampling techniques. Finally, the use of the XDistance performance ranking metric was found to be more effective in ranking several classifiers' performances while offering an indication of class bias, unlike existing performance metrics The overall contributions of this thesis can be summarised as follow. First, several data mining techniques were thoroughly assessed to formulate the new Octopus framework to produce new reliable classifiers. In addition, we offer a further understanding of the impact of newly engineered features, the physical activity index (PAI) and biological effective dose (BED). Second, the newly developed methods within the new framework. Finally, the newly accepted developed predictive models help detect adverse health events, namely, visceral fat-associated diseases and advanced breast cancer radiotherapy toxicity side effects. These contributions could be used to guide future theories, experiments and healthcare interventions in preventive medicine and data mining

Técnicas de mineração incrementais em recuperação de informação

[EN] A desirable property of learning algorithms is the ability of incorporating new data in an incremental way. Incremental algorithms have received attention on the last few years. Particulary Bayesian networks, this is due to the hardness of the task. In Bayesian networks one example can change the whole structure of the Bayesian network. In this theses we focus on incremental induction of Tree Augmented Naive Bayes (TAN) algorithm. A incremental version of TAN saves computing time, is more suite to data mining and concept drift. But, as usual in Bayesian learning TAN is restricted to discrete attributes. Complementary to the incremental TAN, we propose an incremental discretization algorithm, necessary to evaluate TAN in domains with continuous attribute. Discretization is a fundamental pre-processing step for some well- known algorithms, the topic of incremental discretization has received few attention from the community. This theses has two major contributions, the benefict of both proposals is incremental learning, one for TAN and the other for discretization.We present and test a algorithm that rebuilds the network structure of tree augmented naive Bayes (TAN) based on the weighted sum of vectors containing the mutual information. We also present a new discretization method, this works in two layers. This two-stage architecture is very fexible. It can be used as supervised or unsupervised. For the second layer any base discretization method can be used: equal width, equal frequency, recursive entropy discretization, chi-merge, etc. The most relevant aspect is that the boundaries of the intervals of the second layer can change when new data is available. We tested experimentally the incremental approach to discretization with batch and incremental learners. The experimental evaluation of incremental TAN shows a perfor mance similar to the batch version. Similar remarks apply to incremental discretization. This is a relevant aspect, because few works in machine learning address the fundamental aspect of incremental discretization. We believe that with Incremental discretization, the evaluation of the incremental algorithms can become more realistic and accurate. We evaluated two versions of incremental discretization: supervised and unsupervised. We have seen that this feature can improve accuracy for the incremental learners and that the preview of future algorithm performance can be more precise. This method of discretization has another advantages, like, can be used with large data set's or can be used in dynamic environments with concept drift, areas where a batch discretization can be difficult or is not adequate.[ES] Esta tesis tenía como objetivo el estudio de una red Bayesiana (TAN) incremental. Durante el transcurso de esta se verificó la laguna en el área de una discretización incremental para la evaluación de un algoritmo incremental. Así se procuró dar como contribución para el área no solo un clasificador Bayesiano incremental sino también un modo de evaluación correcto del clasificador. Los Sistemas de Recuperación de Información tienen como objetivo la realización de las tareas de indexación, búsqueda y clasificación de documentos (expresos en la forma textual), con el fin de satisfacer la necesidad de información del individuo, generalmente expresa a través de consultas. La necesidad de información puede ser entendida como la búsqueda de respuestas para determinadas cuestiones que tienen que ser resueltas, la recuperación de documentos que tratan sobre un determinado asunto o incluso la relación entre asuntos

Unified processing framework of high-dimensional and overly imbalanced chemical datasets for virtual screening.

Virtual screening in drug discovery involves processing large datasets containing unknown molecules in order to find the ones that are likely to have the desired effects on a biological target, typically a protein receptor or an enzyme. Molecules are thereby classified into active or non-active in relation to the target. Misclassification of molecules in cases such as drug discovery and medical diagnosis is costly, both in time and finances. In the process of discovering a drug, it is mainly the inactive molecules classified as active towards the biological target i.e. false positives that cause a delay in the progress and high late-stage attrition. However, despite the pool of techniques available, the selection of the suitable approach in each situation is still a major challenge. This PhD thesis is designed to develop a pioneering framework which enables the analysis of the virtual screening of chemical compounds datasets in a wide range of settings in a unified fashion. The proposed method provides a better understanding of the dynamics of innovatively combining data processing and classification methods in order to screen massive, potentially high dimensional and overly imbalanced datasets more efficiently