Effective techniques for handling incomplete data using decision trees

Decision Trees (DTs) have been recognized as one of the most successful formalisms for knowledge representation and reasoning and are currently applied to a variety of data mining or knowledge discovery applications, particularly for classification problems. There are several efficient methods to learn a DT from data. However, these methods are often limited to the assumption that data are complete. In this thesis, some contributions to the field of machine learning and statistics that solve the problem of extracting DTs for learning and classification tasks from incomplete databases are presented. The methodology underlying the thesis blends together well-established statistical theories with the most advanced techniques for machine learning and automated reasoning with uncertainty. The first contribution is the extensive simulations which study the impact of missing data on predictive accuracy of existing DTs which can cope with missing values, when missing values are in both the training and test sets or when they are in either of the two sets. All simulations are performed under missing completely at random, missing at random and informatively missing mechanisms and for different missing data patterns and proportions. The proposal of a simple, novel, yet effective proposed procedure for training and testing using decision trees in the presence of missing data is the next contribution. Original and simple splitting criteria for attribute selection in tree building are put forward. The proposed technique is evaluated and validated in empirical tests over many real world application domains. In this work, the proposed algorithm maintains (sometimes exceeds) the outstanding accuracy of multiple imputation, especially on datasets containing mixed attributes and purely nominal attributes. Also, the proposed algorithm greatly improves in accuracy for IM data. Another major advantage of this method over multiple imputation is the important saving in computational resources due to it simplicity. The next contribution is the proposal of three versions of simple probabilistic techniques that could be used for classifying incomplete vectors using decision trees based on complete data. The proposed procedure is superficially similar to that of fractional cases but more effective. The experimental results demonstrate that these approaches can achieve comparative quality to sophisticated algorithms like multiple imputation and therefore are applicable to all kinds of datasets. Finally, novel uses of two proposed ensemble procedures for handling incomplete training and test data are proposed and discussed. The algorithms combine the two best approaches either with resampling (REMIMIA) or without resampling (EMIMIA) of the training data before growing the decision trees. Experiments are used to evaluate and validate the success of the proposed ensemble methods with respect to individual missing data techniques in the form of empirical tests. EMIMIA attains the highest overall level of prediction accuracy

Improving performance through concept formation and conceptual clustering

Research from June 1989 through October 1992 focussed on concept formation, clustering, and supervised learning for purposes of improving the efficiency of problem-solving, planning, and diagnosis. These projects resulted in two dissertations on clustering, explanation-based learning, and means-ends planning, and publications in conferences and workshops, several book chapters, and journals; a complete Bibliography of NASA Ames supported publications is included. The following topics are studied: clustering of explanations and problem-solving experiences; clustering and means-end planning; and diagnosis of space shuttle and space station operating modes

A network-aware framework for energy-efficient data acquisition in wireless sensor networks

Wireless sensor networks enable users to monitor the physical world at an extremely high fidelity. In order to collect the data generated by these tiny-scale devices, the data management community has proposed the utilization of declarative data-acquisition frameworks. While these frameworks have facilitated the energy-efficient retrieval of data from the physical environment, they were agnostic of the underlying network topology and also did not support advanced query processing semantics. In this paper we present KSpot+, a distributed network-aware framework that optimizes network efficiency by combining three components: (i) the tree balancing module, which balances the workload of each sensor node by constructing efficient network topologies; (ii) the workload balancing module, which minimizes data reception inefficiencies by synchronizing the sensor network activity intervals; and (iii) the query processing module, which supports advanced query processing semantics. In order to validate the efficiency of our approach, we have developed a prototype implementation of KSpot+ in nesC and JAVA. In our experimental evaluation, we thoroughly assess the performance of KSpot+ using real datasets and show that KSpot+ provides significant energy reductions under a variety of conditions, thus significantly prolonging the longevity of a WSN