Classification of incomplete data using classifier ensembles

Chen, Haixia; Du, Yuping; Jiang, K.

doi:10.1109/icsai.2012.6223495

Cited by 15 publications

(22 citation statements)

References 10 publications

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“…Chen et al [14] use multiple ensembles to classify incomplete datasets. Their strategy consists in partitioning the incomplete datasets in multiple complete sets and in training the different classifiers on each sample.…”

Section: Related Workmentioning

confidence: 99%

Evolving meta-ensemble of classifiers for handling incomplete and unbalanced datasets in the cyber security domain

Folino

Pisani

2016

Applied Soft Computing

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Section: Related Workmentioning

confidence: 99%

Evolving meta-ensemble of classifiers for handling incomplete and unbalanced datasets in the cyber security domain

Folino

Pisani

2016

Applied Soft Computing

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“…6. Undersampling + multi-classifiers ensemble (EUM): performing clusteringbased stratified under-sampling as proposed by [17]. 7.…”

Section: Imbalanced Sarcasm Classificationmentioning

confidence: 99%

“…Following the simple weight calculation method in [17], our weight formula of sub-classifier C i is:…”

Section: Weighted Voting Strategymentioning

confidence: 99%

Sarcasm Detection in Social Media Based on Imbalanced Classification

Liu

Chen

et al. 2014

Web-Age Information Management

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Abstract. Sarcasm is a pervasive linguistic phenomenon in online documents that express subjective and deeply-felt opinions. Detection of sarcasm is of great importance and beneficial to many NLP applications, such as sentiment analysis, opinion mining and advertising. Current studies consider automatic sarcasm detection as a simple text classification problem. They do not use explicit features to detect sarcasm and ignore the imbalance between sarcastic and non-sarcastic samples in real applications. In this paper, we first explore the characteristics of both English and Chinese sarcastic sentences and introduce a set of features specifically for detecting sarcasm in social media. Then, we propose a novel multi-strategy ensemble learning approach(MSELA) to handle the imbalance problem. We evaluate our proposed model on English and Chinese data sets. Experimental results show that our ensemble approach outperforms the state-of-the-art sarcasm detection approaches and popular imbalanced classification methods.

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“…An ensemble classifier is a classifier consisting of a set of classifiers, and it has been proven to improve classification accuracy [35,120,124]. Robust ensemble classifiers have been conducted by building multiple classifiers working on different features in the training process and then selecting applicable classifiers to classify each incomplete instance without requiring any imputation method [24,125,185]. However, existing ensemble methods for classification with incomplete data often do not work well on datasets with numerous missing values [24,185].…”

Section: Motivationsmentioning

confidence: 99%

“…Another example is to build a set of classifiers, and then choose only applicable classifiers to classify an incomplete instance [24,125]. This approach can save time for estimating missing values, and it does not require any assumption about missing data [24]. However, existing methods are often inaccurate when faced with a large number of missing values.…”

Section: Approaches To Classification With Incomplete Datamentioning

confidence: 99%

Evolutionary Machine Learning for Classification with Incomplete Data

Tran¹

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<p>Classification is a major task in machine learning and data mining. Many real-world datasets suffer from the unavoidable issue of missing values. Classification with incomplete data has to be carefully handled because inadequate treatment of missing values will cause large classification errors. Existing most researchers working on classification with incomplete data focused on improving the effectiveness, but did not adequately address the issue of the efficiency of applying the classifiers to classify unseen instances, which is much more important than the act of creating classifiers. A common approach to classification with incomplete data is to use imputation methods to replace missing values with plausible values before building classifiers and classifying unseen instances. This approach provides complete data which can be then used by any classification algorithm, but sophisticated imputation methods are usually computationally intensive, especially for the application process of classification. Another approach to classification with incomplete data is to build a classifier that can directly work with missing values. This approach does not require time for estimating missing values, but it often generates inaccurate and complex classifiers when faced with numerous missing values. A recent approach to classification with incomplete data which also avoids estimating missing values is to build a set of classifiers which then is used to select applicable classifiers for classifying unseen instances. However, this approach is also often inaccurate and takes a long time to find applicable classifiers when faced with numerous missing values. The overall goal of the thesis is to simultaneously improve the effectiveness and efficiency of classification with incomplete data by using evolutionary machine learning techniques for feature selection, clustering, ensemble learning, feature construction and constructing classifiers. The thesis develops approaches for improving imputation for classification with incomplete data by integrating clustering and feature selection with imputation. The approaches improve both the effectiveness and the efficiency of using imputation for classification with incomplete data. The thesis develops wrapper-based feature selection methods to improve input space for classification algorithms that are able to work directly with incomplete data. The methods not only improve the classification accuracy, but also reduce the complexity of classifiers able to work directly with incomplete data. The thesis develops a feature construction method to improve input space for classification algorithms with incomplete data by proposing interval genetic programming-genetic programming with a set of interval functions. The method improves the classification accuracy and reduces the complexity of classifiers. The thesis develops an ensemble approach to classification with incomplete data by integrating imputation, feature selection, and ensemble learning. The results show that the approach is more accurate, and faster than previous common methods for classification with incomplete data. The thesis develops interval genetic programming to directly evolve classifiers for incomplete data. The results show that classifiers generated by interval genetic programming can be more effective and efficient than classifiers generated the combination of imputation and traditional genetic programming. Interval genetic programming is also more effective than common classification algorithms able to work directly with incomplete data. In summary, the thesis develops a range of approaches for simultaneously improving the effectiveness and efficiency of classification with incomplete data by using a range of evolutionary machine learning techniques.</p>

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Classification of incomplete data using classifier ensembles

Cited by 15 publications

References 10 publications

Evolving meta-ensemble of classifiers for handling incomplete and unbalanced datasets in the cyber security domain

Evolving meta-ensemble of classifiers for handling incomplete and unbalanced datasets in the cyber security domain

Sarcasm Detection in Social Media Based on Imbalanced Classification

Evolutionary Machine Learning for Classification with Incomplete Data

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