List of Titles

Mass estimation and its applications

- Ting, Kaiming, Zhou, Guangtong, Liu, Fei, Chuan, J. T. S.

Authors: Ting, Kaiming , Zhou, Guangtong , Liu, Fei , Chuan, J. T. S.
Date: 2010
Type: Text , Conference paper
Relation: Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining p. 989-998
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Reviewed:
Description: This paper introduces mass estimation--a base modelling mechanism in data mining. It provides the theoretical basis of mass and an efficient method to estimate mass. We show that it solves problems very effectively in tasks such as information retrieval, regression and anomaly detection. The models, which use mass in these three tasks, perform at least as good as and often better than a total of eight state-of-the-art methods in terms of task-specific performance measures. In addition, mass estimation has constant time and space complexities.

Multi-dimensional mass estimation and mass-based clustering

- Ting, Kaiming, Wells, Jonathan

Authors: Ting, Kaiming , Wells, Jonathan
Date: 2010
Type: Text , Conference paper
Relation: Proceedings of the 10th IEEE International Conference on Data Mining (ICDM) p. 511-520
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Description: Mass estimation, an alternative to density estimation, has been shown recently to be an effective base modelling mechanism for three data mining tasks of regression, information retrieval and anomaly detection. This paper advances this work in two directions. First, we generalise the previously proposed one-dimensional mass estimation to multidimensional mass estimation, and significantly reduce the time complexity to O(ψh) from O(ψ h )-making it feasible for a full range of generic problems. Second, we introduce the first clustering method based on mass-it is unique because it does not employ any distance or density measure. The structure of the new mass model enables different parts of a cluster to be identified and merged without expensive evaluations. The characteristics of the new clustering method are: (i) it can identify arbitrary-shape clusters; (ii) it is significantly faster than existing density-based or distance-based methods; and (iii) it is noise-tolerant.
Description: Mass estimation, an alternative to density estimation, has been shown recently to be an effective base modelling mechanism for three data mining tasks of regression, information retrieval and anomaly detection. This paper advances this work in two directions. First, we generalise the previously proposed one-dimensional mass estimation to multidimensional mass estimation, and significantly reduce the time complexity to O(

Boosting support vector machines successfully

- Ting, Kaiming, Zhu, Lian

Authors: Ting, Kaiming , Zhu, Lian
Date: 2009
Type: Text , Conference paper
Relation: 8th International Workshop on Multiple Classifier Systems (MCS 2009) p. 509-518
Full Text: false
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Description: Boosting has been shown to improve the predictive performance of unstable learners such as decision trees, but not of stable learners like support vector machines (SVM). In addition to the model stability problem, the high computational cost of SVM prohibits it from generating multiple models to form an ensemble for large data sets. This paper introduces a method that not only enables boosting to improve the predictive performance of SVM, but also reduces the computational cost to make ensembles of SVM feasible for large data sets. The method proposes to build local models, instead of global models; and it is the first method, to the best of our knowledge, to solve the two problems in boosting SVM at the same time. The proposed method to boost SVM also performs better than boosting decision trees in term of predictive accuracy in our experiments

FaSS : Ensembles for stable learners

- Ting, Kaiming, Wells, Jonathan, Tan, Swee, Teng, Shyh, Webb, Geoffrey

Authors: Ting, Kaiming , Wells, Jonathan , Tan, Swee , Teng, Shyh , Webb, Geoffrey
Date: 2009
Type: Text , Conference paper
Relation: 8th International Workshop on Multipul Classifier Systems (MCS 2009)
Full Text: false
Reviewed:
Description: This paper introduces a new ensemble approach, Feature-Space Subdivision (FaSS), which builds local models instead of global models. FaSS is a generic ensemble approach that can use either stable or unstable models as its base models. In contrast, existing ensemble approaches which employ randomisation can only use unstable models. Our analysis shows that the new approach reduces the execution time to generate a model in an ensemble with an increased level of localisation in FaSS. Our empirical evaluation shows that FaSS performs significantly better than boosting in terms of predictive accuracy, when a stable learner SVM is used as the base learner. The speed up achieved by FaSS makes SVM ensembles a reality that would otherwise infeasible for large data sets, and FaSS SVM performs better than Boosting J48 and Random Forests when SVM is the preferred base learner

Isolation forest

- Liu, Fei, Ting, Kaiming, Zhou, Zhi-Hua

Authors: Liu, Fei , Ting, Kaiming , Zhou, Zhi-Hua
Date: 2008
Type: Text , Conference paper
Relation: Proceedings of the Eighth IEEE International Conference on Data Mining p. 413-422
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Reviewed:
Description: Most existing model-based approaches to anomaly detection construct a profile of normal instances, then identify instances that do not conform to the normal profile as anomalies. This paper proposes a fundamentally different model-based method that explicitly isolates anomalies instead of profiles normal points. To our best knowledge, the concept of isolation has not been explored in current literature. The use of isolation enables the proposed method, iForest, to exploit sub-sampling to an extent that is not feasible in existing methods, creating an algorithm which has a linear time complexity with a low constant and a low memory requirement. Our empirical evaluation shows that iForest performs favourably to ORCA, a near-linear time complexity distance-based method, LOF and random forests in terms of AUC and processing time, and especially in large data sets. iForest also works well in high dimensional problems which have a large number of irrelevant attributes, and in situations where training set does not contain any anomalies.

Issues of grid-cluster retrievals in swarm-based clustering

- Tan, Swee, Ting, Kaiming, Teng, Shyh

Authors: Tan, Swee , Ting, Kaiming , Teng, Shyh
Date: 2008
Type: Text , Conference paper
Relation: Proceedings of the 2008 IEEE World Congress on Computational Intelligence p. 511-518
Full Text: false
Reviewed:
Description: One common approach in swarm-based clustering is to use agents to create a set of clusters on a two-dimensional grid, and then use an existing clustering method to retrieve the clusters on the grid. The second step, which we call grid-cluster retrieval, is an essential step to obtain an explicit partitioning of data. In this study, we highlight the issues in grid-cluster retrievals commonly neglected by researchers, and demonstrate the non-trivial difficulties involved. To tackle the issues, we then evaluate three methods: K-means, hierarchical clustering (Weighted Single-link) and density-based clustering (DBScan). Among the three methods, DBScan is the only method which has not been previously used for grid-cluster retrievals, yet it is shown to be the most suitable method in terms of effectiveness and efficiency.

Spectrum of Variable-Random trees

- Liu, Fei, Ting, Kaiming, Yu, Yang, Zhou, Zhi-Hua

Authors: Liu, Fei , Ting, Kaiming , Yu, Yang , Zhou, Zhi-Hua
Date: 2008
Type: Text , Journal article
Relation: The Journal of Artificial Intelligence Research Vol. 32, no. (2008), p. 355-384
Full Text: false
Reviewed:
Description: In this paper, we show that a continuous spectrum of randomisation exists, in which most existing tree randomisations are only operating around the two ends of the spectrum. That leaves a huge part of the spectrum largely unexplored. We propose a base learner VR-Tree which generates trees with variable-randomness. VR-Trees are able to span from the conventional deterministic trees to the complete-random trees using a probabilistic parameter. Using VR-Trees as the base models, we explore the entire spectrum of randomised ensembles, together with Bagging and Random Subspace. We discover that the two halves of the spectrum have their distinct characteristics; and the understanding of which allows us to propose a new approach in building better decision tree ensembles. We name this approach Coalescence, which coalesces a number of points in the random-half of the spectrum. Coalescence acts as a committee of "experts" to cater for unforeseeable conditions presented in training data. Coalescence is found to perform better than any single operating point in the spectrum, without the need to tune to a specific level of randomness. In our empirical study, Coalescence ranks top among the benchmarking ensemble methods including Random Forests, Random Subspace and C5 Boosting; and only Coalescence is significantly better than Bagging and Max-Diverse Ensemble among all the methods in the comparison. Although Coalescence is not significantly better than Random Forests, we have identified conditions under which one will perform better than the other.

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