A selective neural network ensemble classification for incomplete data

Yan, Yuanting; Zhang, Yanping; Zhang, Yiwen; Du, Xiujuan

doi:10.1007/s13042-016-0524-0

Cited by 22 publications

(22 citation statements)

References 23 publications

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“…Yan et al proposed a Selective Neural Network Ensemble (SNNE) as a classification method to deal with incomplete datasets [ 31 ]. They investigated the performance of SNNE on 12 UCI datasets.…”

Section: Discussionmentioning

confidence: 99%

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An Efficient and Effective Model to Handle Missing Data in Classification

Mehrabani-Zeinabad

Doostfatemeh

Ayatollahi

2020

BioMed Research International

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Missing data is one of the most important causes in reduction of classification accuracy. Many real datasets suffer from missing values, especially in medical sciences. Imputation is a common way to deal with incomplete datasets. There are various imputation methods that can be applied, and the choice of the best method depends on the dataset conditions such as sample size, missing percent, and missing mechanism. Therefore, the better solution is to classify incomplete datasets without imputation and without any loss of information. The structure of the “Bayesian additive regression trees” (BART) model is improved with the “Missingness Incorporated in Attributes” approach to solve its inefficiency in handling the missingness problem. Implementation of MIA-within-BART is named “BART.m”. As the abilities of BART.m are not investigated in classification of incomplete datasets, this simulation-based study aimed to provide such resource. The results indicate that BART.m can be used even for datasets with 90 missing present and more importantly, it diagnoses the irrelevant variables and removes them by its own. BART.m outperforms common models for classification with incomplete data, according to accuracy and computational time. Based on the revealed properties, it can be said that BART.m is a high accuracy model in classification of incomplete datasets which avoids any assumptions and preprocess steps.

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Section: Discussionmentioning

confidence: 99%

“…For handling missing data in a classification field, there are approaches which handle missing data without any imputation or loss of information [ 30 , 31 ]. As these approaches do not face selection of the proper imputation method, they are much more user-friendly and practical.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Effective Model to Handle Missing Data in Classification

Mehrabani-Zeinabad

Doostfatemeh

Ayatollahi

2020

BioMed Research International

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“…These methods achieve dataset integrity by estimating the value of missing data [10][11][12][13]. In addition, to make the best use of existing data without adding or deleting data, methods of data mining based on the missing dataset have also been proposed, for example, the artificial neural network method [14,15], or the rough set reasoning method [16,17].…”

Section: Introductionmentioning

confidence: 99%

Missing Data Probability Estimation-Based Bayesian Outlier Detection for Plant-Wide Processes with Multisampling Rates

2018

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Traditional outlier detection methods assume that the sampling time and interval are the same. However, for plant-wide processes, since the signal change rate of different devices may vary by several orders of magnitude, the measured data in real-world systems usually have different sampling rates, resulting in missing data. To achieve reliable outlier detection, a missing data probability estimation-based Bayesian outlier detection method is adopted. In this strategy, the expectation–maximization (EM) algorithm is first used to estimate the likelihood probability of different evidence under different process statuses by using the history dataset which contains complete and incomplete samplings. Secondly, the realization of unavailable parts in the monitoring point is estimated as a probability through historical data and online moving horizon data. Bayesian theory and likelihood probability are then used to calculate the outlier posterior probability of different realization. Finally, the outlier probability of the monitoring sampling is calculated by the probability of different realizations and the corresponding outlier probability. Using the Tennessee Eastman (TE) dataset, a simulation indicates that the proposed method exhibits a significant improvement over the complete data method.

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“…Using a powerful imputation method to generate high quality complete training data for building classifiers results in more accurate classifiers. In contrast, existing ensemble methods based on missing patterns [24,185] do not use any imputation, so the training set for each classifier may be as small as a single instances which leads to low accuracy. However, good imputation methods such as multiple imputation are computationally expensive.…”

Section: Overall Proposed Methodsmentioning

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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A selective neural network ensemble classification for incomplete data

Cited by 22 publications

References 23 publications

An Efficient and Effective Model to Handle Missing Data in Classification

An Efficient and Effective Model to Handle Missing Data in Classification

Missing Data Probability Estimation-Based Bayesian Outlier Detection for Plant-Wide Processes with Multisampling Rates

Evolutionary Machine Learning for Classification with Incomplete Data

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