A generic ensemble approach to estimate multidimensional likelihood in Bayesian classifier learning
- Authors: Aryal, Sunil , Ting, Kaiming
- Date: 2016
- Type: Text , Journal article
- Relation: Computational Intelligence Vol. 32, no. 3 (2016), p. 458-479
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- Description: In Bayesian classifier learning, estimating the joint probability distribution (,) or the likelihood (|) directly from training data is considered to be difficult, especially in large multidimensional data sets. To circumvent this difficulty, existing Bayesian classifiers such as Naive Bayes, BayesNet, and ADE have focused on estimating simplified surrogates of (,) from different forms of one‐dimensional likelihoods. Contrary to the perceived difficulty in multidimensional likelihood estimation, we present a simple generic ensemble approach to estimate multidimensional likelihood directly from data. The idea is to aggregate (|) estimated from a random subsample of data . This article presents two ways to estimate multidimensional likelihoods using the proposed generic approach and introduces two new Bayesian classifiers called and that estimate (|) using a nearest‐neighbor density estimation and a probability estimation through feature space partitioning, respectively. Unlike the existing Bayesian classifiers, ENNBayes and MassBayes have constant training time and space complexities and they scale better than existing Bayesian classifiers in very large data sets. Our empirical evaluation shows that ENNBayes and MassBayes yield better predictive accuracy than the existing Bayesian classifiers in benchmark data sets.
Beyond tf-idf and cosine distance in documents dissimilarity measure
- Authors: Aryal, Sunil , Ting, Kaiming , Haffari, Gholamreza , Washio, Takashi
- Date: 2015
- Type: Text , Conference proceedings
- Relation: Asia Information Retrieval Symposium 2015 - Queensland University of Technology, Brisbane, Australia, Brisbane, 2nd-4th Dec, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9460 Vol. 9460, p. 400-406
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Data-dependent dissimilarity measure : An effective alternative to geometric distance measures
- Authors: Aryal, Sunil , Ting, Kaiming , Washio, Takashi , Haffari, Gholamreza
- Date: 2017
- Type: Text , Journal article
- Relation: Knowledge and Information Systems Vol. 53, no. 2 (2017), p. 479-506
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- Description: Nearest neighbor search is a core process in many data mining algorithms. Finding reliable closest matches of a test instance is still a challenging task as the effectiveness of many general-purpose distance measures such as ℓp -norm decreases as the number of dimensions increases. Their performances vary significantly in different data distributions. This is mainly because they compute the distance between two instances solely based on their geometric positions in the feature space, and data distribution has no influence on the distance measure. This paper presents a simple data-dependent general-purpose dissimilarity measure called ‘ mp -dissimilarity’. Rather than relying on geometric distance, it measures the dissimilarity between two instances as a probability mass in a region that encloses the two instances in every dimension. It deems two instances in a sparse region to be more similar than two instances of equal inter-point geometric distance in a dense region. Our empirical results in k-NN classification and content-based multimedia information retrieval tasks show that the proposed mp -dissimilarity measure produces better task-specific performance than existing widely used general-purpose distance measures such as ℓp -norm and cosine distance across a wide range of moderate- to high-dimensional data sets with continuous only, discrete only, and mixed attributes.
Defying the gravity of learning curve : A characteristic of nearest neighbour anomaly detectors
- Authors: Ting, Kaiming , Washio, Takashi , Wells, Jonathan , Aryal, Sunil
- Date: 2017
- Type: Text , Journal article
- Relation: Machine Learning Vol. 106, no. 1 (2017), p. 55-91
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- Description: Conventional wisdom in machine learning says that all algorithms are expected to follow the trajectory of a learning curve which is often colloquially referred to as ‘more data the better’. We call this ‘the gravity of learning curve’, and it is assumed that no learning algorithms are ‘gravity-defiant’. Contrary to the conventional wisdom, this paper provides the theoretical analysis and the empirical evidence that nearest neighbour anomaly detectors are gravity-defiant algorithms.
DEMass: a new density estimator for big data
- Authors: Ting, Kaiming , Washio, Takashi , Wells, Jonathan , Liu, Fei , Aryal, Sunil
- Date: 2013
- Type: Text , Journal article
- Relation: Knowledge and Information Systems Vol. 35, no. 3 (2013), p. 493-524
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- Description: Density estimation is the ubiquitous base modelling mechanism employed for many tasks including clustering, classification, anomaly detection and information retrieval. Commonly used density estimation methods such as kernel density estimator and k-nearest neighbour density estimator have high time and space complexities which render them inapplicable in problems with big data. This weakness sets the fundamental limit in existing algorithms for all these tasks. We propose the first density estimation method, having average case sub-linear time complexity and constant space complexity in the number of instances, that stretches this fundamental limit to an extent that dealing with millions of data can now be done easily and quickly. We provide an asymptotic analysis of the new density estimator and verify the generality of the method by replacing existing density estimators with the new one in three current density-based algorithms, namely DBSCAN, LOF and Bayesian classifiers, representing three different data mining tasks of clustering, anomaly detection and classification. Our empirical evaluation results show that the new density estimation method significantly improves their time and space complexities, while maintaining or improving their task-specific performances in clustering, anomaly detection and classification. The new method empowers these algorithms, currently limited to small data size only, to process big data—setting a new benchmark for what density-based algorithms can achieve.
Improving iForest with relative mass
- Authors: Aryal, Sunil , Ting, Kaiming , Wells, Jonathan , Washio, Takashi
- Date: 2014
- Type: Text , Conference paper
- Relation: 18th Pacific-Asia Conference, PAKDD 2014: Advances in Knowledge Discovery and Data Mining; Tainan, Taiwan; 13th-16th May 2014; published in Lecture Notes in Artificial Intelligence (subseries of Lecture Notes in Computer Science) Vol. 8444, p. 510-521
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- Description: iForest uses a collection of isolation trees to detect anomalies. While it is effective in detecting global anomalies, it fails to detect local anomalies in data sets having multiple clusters of normal instances because the local anomalies are masked by normal clusters of similar density and they become less susceptible to isolation. In this paper, we propose a very simple but effective solution to overcome this limitation by replacing the global ranking measure based on path length with a local ranking measure based on relative mass that takes local data distribution into consideration. We demonstrate the utility of relative mass by improving the task specific performance of iForest in anomaly detection and information retrieval tasks.
MassBayes: a new generative classifier with multi-dimensional likelihood estimation
- Authors: Aryal, Sunil , Ting, Kaiming
- Date: 2013
- Type: Text , Conference paper
- Relation: Advances in Knowledge Discovery and Data Mining: 17th Pacific-Asia Conference p. 136-148
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- Description: Existing generative classifiers (e.g., BayesNet and AnDE) make independence assumptions and estimate one-dimensional likelihood. This paper presents a new generative classifier called MassBayes that estimates multi-dimensional likelihood without making any explicit assumptions. It aggregates the multi-dimensional likelihoods estimated from random subsets of the training data using varying size random feature subsets. Our empirical evaluations show that MassBayes yields better classification accuracy than the existing generative classifiers in large data sets. As it works with fixed-size subsets of training data, it has constant training time complexity and constant space complexity, and it can easily scale up to very large data sets.
Mp-dissimilarity : A data dependent dissimilarity measure
- Authors: Aryal, Sunil , Ting, Kaiming , Haffari, Gholamreza , Washio, Takashi
- Date: 2014
- Type: Text , Conference paper
- Relation: 14th IEEE International Conference on Data Mining (2014 ICDM); Shenzhen, China; 14th-17th December 2014 p. 707-712
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- Description: Nearest neighbour search is a core process in many data mining algorithms. Finding reliable closest matches of a query in a high dimensional space is still a challenging task. This is because the effectiveness of many dissimilarity measures, that are based on a geometric model, such as lp-norm, decreases as the number of dimensions increases. In this paper, we examine how the data distribution can be exploited to measure dissimilarity between two instances and propose a new data dependent dissimilarity measure called 'mp-dissimilarity'. Rather than relying on geometric distance, it measures the dissimilarity between two instances in each dimension as a probability mass in a region that encloses the two instances. It deems the two instances in a sparse region to be more similar than two instances in a dense region, though these two pairs of instances have the same geometric distance. Our empirical results show that the proposed dissimilarity measure indeed provides a reliable nearest neighbour search in high dimensional spaces, particularly in sparse data. Mp-dissimilarity produced better task specific performance than lp-norm and cosine distance in classification and information retrieval tasks.
Revisiting attribute independence assumption in probabilistic unsupervised anomaly detection
- Authors: Aryal, Sunil , Ting, Kaiming , Haffari, Gholamreza
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 11th Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2016 - Auckland, New Zealand, 19th April, 2016 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9650 p. 73-86
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- Description: In this paper, we revisit the simple probabilistic approach of unsupervised anomaly detection by estimating multivariate probability as a product of univariate probabilities, assuming attributes are generated independently. We show that this simple traditional approach performs competitively to or better than five state-of-the-art unsupervised anomaly detection methods across a wide range of data sets from categorical, numeric or mixed domains. It is arguably the fastest anomaly detector. It is one order of magnitude faster than the fastest state-of-the- art method in high dimensional data sets.