397 research outputs found
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Random Prism: An Alternative to Random Forests.
No full textEnsemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is the Prism family of algorithms. Prism algorithms produce modular classification rules that do not necessarily fit into a decision tree structure. Prism classification rulesets achieve a comparable and sometimes higher classification accuracy compared with decision tree classifiers, if the data is noisy and large. Yet Prism still suffers from overfitting on noisy and large datasets. In practice ensemble techniques tend to reduce the overfitting, however there exists no ensemble learner for modular classification rule inducers such as the Prism family of algorithms. This article describes the first development of an ensemble learner based on the Prism family of algorithms in order to enhance Prism’s classification accuracy by reducing overfitting
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Induction of modular classification rules: using Jmax-pruning
No full textThe Prism family of algorithms induces modular classification rules which, in contrast to decision tree induction algorithms, do not necessarily fit together into a decision tree structure. Classifiers induced by Prism algorithms achieve a comparable accuracy compared with decision trees and in some cases even outperform decision trees. Both kinds of algorithms tend to overfit on large and noisy datasets and this has led to the development of pruning methods. Pruning methods use various metrics to truncate decision trees or to eliminate whole rules or single rule terms from a Prism rule set. For decision trees many pre-pruning and postpruning methods exist, however for Prism algorithms only one pre-pruning method has been developed, J-pruning. Recent work with Prism algorithms examined J-pruning in the context of very large datasets and found that the current method does not use its full potential. This paper revisits the J-pruning method for the Prism family of algorithms and develops a new pruning method Jmax-pruning, discusses it in theoretical terms and evaluates it empirically
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J-PMCRI: a methodology for inducing pre-pruned modular classification rules
No full textInducing rules from very large datasets is one of the most challenging areas in data mining. Several approaches exist to scaling up classification rule induction to large datasets, namely data reduction and the parallelisation of classification rule induction algorithms. In the area of parallelisation of classification rule induction algorithms most of the work has been concentrated on the Top Down Induction of Decision Trees (TDIDT), also known as the ‘divide and conquer’ approach. However powerful alternative algorithms exist that induce modular rules. Most of these alternative algorithms follow the ‘separate and conquer’ approach of inducing rules, but very little work has been done to make the ‘separate and conquer’ approach scale better on large training data. This paper examines the potential of the recently developed blackboard based J-PMCRI methodology for parallelising modular classification rule induction algorithms that follow the ‘separate and conquer’ approach. A concrete implementation of the methodology is evaluated empirically on very large datasets
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P-Prism: a computationally efficient approach to scaling up classification rule induction
No full textTop Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unseen data. Alternative algorithms have been developed such as the Prism algorithm. Prism constructs modular rules which produce qualitatively better rules than rules induced by TDIDT. However, along with the increasing size of databases, many existing rule learning algorithms have proved to be computational expensive on large datasets. To tackle the problem of scalability, parallel classification rule induction algorithms have been introduced. As TDIDT is the most popular classifier, even though there are strongly competitive alternative algorithms, most parallel approaches to inducing classification rules are based on TDIDT. In this paper we describe work on a distributed classifier that induces classification rules in a parallel manner based on Prism
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Parallel induction of modular classification rules
No full textThe Distributed Rule Induction (DRI) project at the University of Portsmouth is concerned with distributed data mining algorithms for automatically generating rules of all kinds. In this paper we present a system architecture and its implementation for inducing modular classification rules in parallel in a local area network using a distributed blackboard system. We present initial results of a prototype implementation based on the Prism algorithm
Mystic Flu: poetry and short stories by Julia Gordon-Bramer
No full textMystic Flu is a collection of poetry and short stories by Julia Gordon-Bramer. The poetry and prose of Mystic Flu turns its world inside-out: exploring themes of family, friends, and lovers turned to enemies, and predators; alcoholism and addiction; the subconscious ruling the conscious; the physical wrestling with the spiritual; and the road toward self-actualization in all its strangeness. The poetry in this work is predominantly free verse, confessional, yet embedded with spiritual imagery and double-meanings. The short stories of Mystic Flu are realistic, with thematic views from the skewed wisdom and fleeting innocence of the child’s experience, moving toward the definite unknowing and inevitable corruption of adulthood.
Note: This thesis is under a author requested embargo until 2210, after which it will become available in accordance with end of expected copyright protection. This embargo year is intentional and not an error
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Improving modular classification rule induction with G-Prism using dynamic rule term boundaries
No full textModular classification rule induction for predictive analytics is an alternative and expressive approach to rule induction as opposed to decision tree based classifiers. Prism classifiers achieve a similar classification accuracy compared with decision trees, but tend to overfit less, especially if there is noise in the data. This paper describes the development of a new member of the Prism family, the G-Prism classifier, which improves the classification performance of the classifier. G-Prism is different compared with the remaining members of the Prism family as it follows a different rule term induction strategy. G-Prism’s rule term induction strategy is based on Gauss Probability Density Distribution (GPDD) of target classes rather than simple binary splits (local discretisation). Two versions of G-Prism have been developed, one uses fixed boundaries to build rule terms from GPDD and the other uses dynamic rule term boundaries. Both versions have been compared empirically against Prism on 11 datasets using various evaluation metrics. The results show that in most cases both versions of G-Prism, especially G-Prism with dynamic boundaries, achieve a better classification performance compared with Prism
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Parallel random prism: a computationally efficient ensemble learner for classification
No full textGenerally classifiers tend to overfit if there is noise in the training data or there are missing values. Ensemble learning methods are often used to improve a classifier's classification accuracy. Most ensemble learning approaches aim to improve the classification accuracy of decision trees. However, alternative classifiers to decision trees exist. The recently developed Random Prism ensemble learner for classification aims to improve an alternative classification rule induction approach, the Prism family of algorithms, which addresses some of the limitations of decision trees. However, Random Prism suffers like any ensemble learner from a high computational overhead due to replication of the data and the induction of multiple base classifiers. Hence even modest sized datasets may impose a computational challenge to ensemble learners such as Random Prism. Parallelism is often used to scale up algorithms to deal with large datasets. This paper investigates parallelisation for Random Prism, implements a prototype and evaluates it empirically using a Hadoop computing cluster
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eRules: a modular adaptive classification rule learning algorithm for data streams
No full textAdvances in hardware and software in the past decade allow to capture, record and process fast data streams at a large scale. The research area of data stream mining has emerged as a consequence from these advances in order to cope with the real time analysis of potentially large and changing data streams. Examples of data streams include Google searches, credit card transactions, telemetric data and data of continuous chemical production processes. In some cases the data can be processed in batches by traditional data mining approaches. However, in some applications it is required to analyse the data in real time as soon as it is being captured. Such cases are for example if the data stream is infinite, fast changing, or simply too large in size to be stored. One of the most important data mining techniques on data streams is classification. This involves training the classifier on the data stream in real time and adapting it to concept drifts. Most data stream classifiers are based on decision trees. However, it is well known in the data mining community that there is no single optimal algorithm. An algorithm may work well on one or several datasets but badly on others. This paper introduces eRules, a new rule based adaptive classifier for data streams, based on an evolving set of Rules. eRules induces a set of rules that is constantly evaluated and adapted to changes in the data stream by adding new and removing old rules. It is different from the more popular decision tree based classifiers as it tends to leave data instances rather unclassified than forcing a classification that could be wrong. The ongoing development of eRules aims to improve its accuracy further through dynamic parameter setting which will also address the problem of changing feature domain values
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