105,659 research outputs found
TESTING DECISION TREE ALGORITHM USING TANAGRA APPLICATIONS
Decision tree is one algorithm that is used to classify segmentation or grouping which is predictive, Decision Tree Algorithm has the Advantages in processing numerical (continuous) and discrete data, can handle missing attribute values, produces rules that are easily interpreted and the fastest among other algorithms. Prediction accuracy is the ability of the model to be able to predict class labels against new or previously unknown data well. In terms of speed or computational time efficiency needed to create and use a model. The application used is Tanagra because the application is available for Decision tree architecture
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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
Predictive Capacity of Meteorological Data - Will it rain tomorrow
With the availability of high precision digital sensors and cheap storage
medium, it is not uncommon to find large amounts of data collected on almost
all measurable attributes, both in nature and man-made habitats. Weather in
particular has been an area of keen interest for researchers to develop more
accurate and reliable prediction models. This paper presents a set of
experiments which involve the use of prevalent machine learning techniques to
build models to predict the day of the week given the weather data for that
particular day i.e. temperature, wind, rain etc., and test their reliability
across four cities in Australia {Brisbane, Adelaide, Perth, Hobart}. The
results provide a comparison of accuracy of these machine learning techniques
and their reliability to predict the day of the week by analysing the weather
data. We then apply the models to predict weather conditions based on the
available data.Comment: 7 pages, 2 Result Set
A review of associative classification mining
Associative classification mining is a promising approach in data mining that utilizes the
association rule discovery techniques to construct classification systems, also known as
associative classifiers. In the last few years, a number of associative classification algorithms
have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms
employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule
evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative
classification techniques with regards to the above criteria. Finally, future directions in associative
classification, such as incremental learning and mining low-quality data sets, are also
highlighted in this paper
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