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Parameter inference of cost-sensitive boosting algorithms

引用

4th International Conference on machine Learning and Data Mining in pattern Recognition, MLDM 2005

作者： Sun, Yanmin Wong, A.K.C. Wang, Yang Pattern Analysis and Machine Intelligence Lab. University of Waterloo Canada Pattern Discovery Software Ltd.

ISBN: (纸本)3540269231

Several cost-sensitive boosting algorithms have been reported as effective methods in dealing with class imbalance problem. Misclassification costs, which reflect the different level of class identification importance, are integrated into the weight update formula of AdaBoost algorithm. Yet, it has been shown that the weight update parameter of AdaBoost is induced so as the training error can be reduced most rapidly. This is the most crucial step of AdaBoost in converting a weak learning algorithm into a strong one. However, most reported cost-sensitive boosting algorithms ignore such a property. In this paper, we come up with three versions of cost-sensitive AdaBoost algorithms where the parameters for sample weight updating are induced. Then, their identification abilities on the small classes are tested on four "real world" medical data sets taken from UCI machine Learning Database based on F-measure. Our experimental results show that one of our proposed costsensitive AdaBoost algorithms is superior in achieving the best identification ability on the small class among all reported cost-sensitive boosting algorithms. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Algorithms

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Document classification using phrases

引用

Joint IAPR 9th International Workshop on Structural and Syntactic pattern Recognition, SSPR 2002 and 4th International Workshop on Statistical Techniques in pattern Recognition, SPR 2002

作者： Bakus, Jan Kamel, Mohamed Pattern Analysis and Machine Intelligence Lab Department of Systems Design Engineering University of Waterloo WaterlooON Canada

ISBN: (纸本)3540440119

This paper presents a Bayes document classifier using phrases as *** e phrases are extracted using a grammar that iteratively applies the rules to the sequence of words in the document. This grammar is generated from a training set using statistical word association. W e report an improvement in the classification over the "bag of words" representation. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Classification (of information)

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Particle swarm clustering ensemble 08

Particle swarm clustering ensemble

引用

10th Annual Genetic and Evolutionary Computation Conference, GECCO 2008

作者： Ahmadi, Abbas Karray, Fakhri Kamel, Mohamed Pattern Analysis and Machine Intelligence Lab. Department of Systems Design Engineering University of Waterloo 200 University Avenue West Waterloo ON Canada

ISBN: (纸本)9781605581309

Extracting natural groups of the unlabeled data is known as clustering. To improve the stability and robustness of the clustering outputs, clustering ensembles have emerged recently. In this paper, an ensemble of particle swarm clustering algorithms is proposed. That is, the members of the ensemble are based on the cooperative swarms clustering approaches. The performance of the proposed particle swarm clustering ensemble is evaluated using different data sets and is compared to that of other clustering techniques.

关键词： Particle swarm optimization (PSO)

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Topic discovery from text using aggregation of different clustering methods 15th

Topic discovery from text using aggregation of different clu...

引用

15th Conference of the Canadian Society for Computational Studies of intelligence, AI 2002

作者： Ayad, Hanan Kamel, Mohamed Pattern Analysis and Machine Intelligence Lab Systems Design Engineering University of Waterloo WaterlooONN2L 3G1 Canada

ISBN: (纸本)9783540479222

Cluster analysis is an un-supervised learning technique that is widely used in the process of topic discovery from text. The research presented here proposes a novel un-supervised learning approach based on aggregation of clusterings produced by different clustering techniques. By examining and combining two different clusterings of a document collection, the aggregation aims at revealing a better structure of the data rather than imposing one that is imposed or constrained by the clustering method itself. When clusters of documents are formed, a process called topic extraction picks terms from the feature space (i.e. the vocabulary of the whole collection) to describe the topic of each cluster. It is proposed at this stage to re-compute terms weights according to the revealed cluster structure. The work further investigates the adaptive setup of the parameters required for the clustering and aggregation techniques. Finally, a topic accuracy measure is developed and used along with the F-measure to evaluate and compare the extracted topics and the clustering quality (respectively) before and after the aggregation. Experimental evaluation shows that the aggregation can successfully improve the clustering quality and the topic accuracy over individual clustering techniques. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Cluster analysis

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Extraction of text phrases using hierarchical grammar 15th

Extraction of text phrases using hierarchical grammar

引用

15th Conference of the Canadian Society for Computational Studies of intelligence, AI 2002

作者： Bakus, Jan Kamel, Mohamed Carey, Tom Pattern Analysis and Machine Intelligence Lab Department of Systems Design Engineering University of Waterloo WaterlooON Canada Department of Management Sciences University of Waterloo WaterlooON Canada

ISBN: (纸本)9783540479222

This paper presents an algorithm for extraction of phrases from text *** e algorithm builds phrases by iteratively merging bigrams according to an association *** o association measures are presented: mutual information and t-test.T he extracted phrases are tested in a document classification task using a tf/idf model and a k-nearest neighbor classifier. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Extraction

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Design of multiple classifier systems for time series data

Design of multiple classifier systems for time series data

引用

6th International Workshop on Multiple Classifier Systems, MCS 2005

作者： Chen, Lei Kamel, Mohamed S. Pattern Analysis and Machine Intelligence Lab Electrical and Computer Engineering University of Waterloo Ont. N2L 3G1 Canada

In previous work, we showed that the use of Multiple Input Representation(MIR) for the classification of time series data provides complementary information that leads to better accuracy. [4]. In this paper, we introduce the Static Minimization-Maximization approach to build Multiple Classifier Systems(MCSs) using MIR. SMM consists of two steps. In the minimization step, a greedy algorithm is employed to iteratively select the classifiers from the knowledge space to minimize the training error of MCSs. In the maximization step, a modified version of Behavior Knowledge Space(BKS), Balanced Behavior Knowledge Space(BBKS), is used to maximize the expected accuracy of the whole system given that the training error is minimized. Several popular techniques including AdaBoost, Bagging and Random Subspace are used as the benchmark to evaluate the proposed approach on four time series data sets. The results obtained from our experiments show that the performance of the proposed approach is effective as well as robust for the classification of time series data. In addition, this approach could be further extended to other applications in our future research. © Springer-Verlag Berlin Heidelberg 2005.

关键词： pattern recognition

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Feature based decision fusion 2nd

引用

2nd International Conference on Advances in pattern Recognition, ICAPR 2001

作者： Wanas, Nayer M. Kamel, Mohamed S. Pattern Analysis and Machine Intelligence lab Department of Systems Design Engineering University of Waterloo WaterlooONN2L-3G1 Canada

ISBN: (纸本)3540417672

In this paper we present a new architecture for combining classifiers. This approach integrates learning into the voting scheme used to aggregate individual classifiers decisions. This overcomes the drawbacks of having static voting techniques. The focus of this work is to make the decision fusion a more adaptive process. This approach makes use of feature detectors responsible for gathering information about the input to perform adaptive decision aggregation. Test results show improvement in the overall classification rates over any individual classifier, as well as different static classifier-combining schemes. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Network architecture

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Urban land-cover classification based on Swarm intelligence from high resolution remote sensing imagery

Urban land-cover classification based on Swarm intelligence ...

引用

International Conference on Remote Sensing, Environment and Transportation Engineering

作者： Bedawi, Safaa M. Kamel, Mohamed S. Pattern Analysis and Machine Intelligence Lab. Department of Electrical and Computer Engineering University of Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781424491711

Urban land-cover classification is one of the most challenging problems in pattern analysis and machine intelligence systems in remote sensing. Dense urban environment sensed by very high-resolution (VHR) optical sensors is even more challenging. Occlusions and shadows due to buildings and trees hide some objects in the scene. Despite its simplicity and usefulness, conventional classification methods have failed to have a high classification accuracy in dense urban areas. The objective of this study is to improve the quality of the land-cover classification. We propose using a Particle Swarm Optimization (PSO) based algorithm for the classification of VHR (0.1 to 1 m) remote sensing data over urban areas. This method is to discover classification rules through simulating the social behavior of animals such as the behaviours of bird flocking. The results for aerial images classification show the significance of this method. PSO-based classifier has been applied to the classification of remote sensing data in dense urban district of Kitchener-Waterloo and has achieved high predictive accuracy of 90 %. © 2011 IEEE.

关键词： Image classification

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Cluster-based cumulative ensembles

Cluster-based cumulative ensembles

引用

6th International Workshop on Multiple Classifier Systems, MCS 2005

作者： Ayad, Hanan G. Kamel, Mohamed S. Pattern Analysis and Machine Intelligence Lab Electrical and Computer Engineering University of Waterloo Waterloo Ont. N2L 3G1 Canada

In this paper, we propose a cluster-based cumulative representation for cluster ensembles. Cluster labels are mapped to incrementally accumulated clusters, and a matching criterion based on maximum similarity is used. The ensemble method is investigated with boot-strap re-sampling, where the k-means algorithm is used to generate high granularity clusterings. For combining, group average hierarchical metaclustering is applied and the Jaccard measure is used for cluster similarity computation. patterns are assigned to combined meta-clusters based on estimated cluster assignment probabilities. The cluster-based cumulative ensembles are more compact than co-association-based ensembles. Experimental results on artificial and real data show reduction of the error rate across varying ensemble parameters and cluster structures. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Classification (of information)

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Learning term dependency links using information theoretic inclusion measure

Learning term dependency links using information theoretic i...

引用

17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007

作者： Makrehchi, Masoud Kamel, Mohamed S. Pattern Analysis and Machine Intelligence Lab. Department of Electrical and Computer Engineering University of Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)0769530192

An algorithm to identify and remove term redundancy is proposed for text classifiers using ranking-based feature selection. The proposed method employs a normalized mutual information, which is called inclusion measure, to estimate asymmetric dependency between two terms. Based on pair-wise dependency measures, a dependency matrix is constructed. In this paper, an algorithm is proposed to learn term dependency links from term dependency matrix, and visualize the dependency between term in a graph called term dependency tree. All nodes of the tree are categorized into two groups: hubs and links. Any node whose outdegree is less than two will join the Links group. We show that all link nodes are most likely redundant. We also introduce a criterion, which is called substitution cost, to decide whether to remove or retain a candidate, redundant term. The proposed approach is applied to four well-known benchmark data sets with a SVM and Rocchio classifier using a set of highly aggressive feature selection schemes. The results show the effectiveness of the proposed method especially when applied to weak classifiers. © 2007 IEEE.

关键词： Forestry

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