Machine Learning Approach-based Big Data Imputation Methods for Outdoor Air Quality Forecasting

Missing data from ambient air databases is a typical issue, but it is much worse in small towns or cities. Missing data is a significant concern for environmental epidemiology. These settings have high pollution exposure levels worldwide, and dataset gaps obstruct health investigations that could later affect local and international policies. When a substantial number of observations contain missing values, the standard errors increase due to the smaller sample size, which may significantly affect the final result. Generally, the performance of various missing value imputation algorithms is proportional to the size of the database and the percentage of missing values within it. This paper proposes and demonstrates an ensemble – imputation – classification framework approach to rebuild air quality information using a dataset from Beijing, China, to forecast air quality. Various single and multiple imputation procedures are utilized to fill the missing records. Then ensemble of diverse classifiers is used on the imputed data to find the air pollution level. The recommended model aims to reduce the error rate and improve accuracy. Extensive testing of datasets with actual missing values has revealed that the suggested methodology significantly enhances the air quality forecasting model’s accuracy with multiple imputation and ensemble techniques when compared to other conventional single imputation techniques

Fermi LAT AGN classification using supervised machine learning

Classifying Active Galactic Nuclei (AGN) is a challenge, especially for BL Lac Objects (BLLs), which are identified by their weak emission line spectra. To address the problem of classification, we use data from the 4th Fermi Catalog, Data Release 3. Missing data hinders the use of machine learning to classify AGN. A previous paper found that Multiple Imputation by Chain Equations (MICE) imputation is useful for estimating missing values. Since many AGN have missing redshift and the highest energy, we use data imputation with MICE and K-nearest neighbor (kNN) algorithm to fill in these missing variables. Then, we classify AGN into the BLLs or the Flat Spectrum Radio Quasars (FSRQs) using the SuperLearner, an ensemble method that includes several classification algorithms like logistic regression, support vector classifiers, Random Forests, Ranger Random Forests, multivariate adaptive regression spline (MARS), Bayesian regression, Extreme Gradient Boosting. We find that a SuperLearner model using MARS regression and Random Forests algorithms is 91.1% accurate for kNN imputed data and 91.2% for MICE imputed data. Furthermore, the kNN-imputed SuperLearner model predicts that 892 of the 1519 unclassified blazars are BLLs and 627 are Flat Spectrum Radio Quasars (FSRQs), while the MICE-imputed SuperLearner model predicts 890 BLLs and 629 FSRQs in the unclassified set. Thus, we can conclude that both imputation methods work efficiently and with high accuracy and that our methodology ushers the way for using SuperLearner as a novel classification method in the AGN community and, in general, in the astrophysics community.Comment: 15 pages, 8 figures, to be published in Monthly Notices of the Royal Astronomical Societ

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Effective techniques for handling incomplete data using decision trees

Decision Trees (DTs) have been recognized as one of the most successful formalisms for knowledge representation and reasoning and are currently applied to a variety of data mining or knowledge discovery applications, particularly for classification problems. There are several efficient methods to learn a DT from data. However, these methods are often limited to the assumption that data are complete. In this thesis, some contributions to the field of machine learning and statistics that solve the problem of extracting DTs for learning and classification tasks from incomplete databases are presented. The methodology underlying the thesis blends together well-established statistical theories with the most advanced techniques for machine learning and automated reasoning with uncertainty. The first contribution is the extensive simulations which study the impact of missing data on predictive accuracy of existing DTs which can cope with missing values, when missing values are in both the training and test sets or when they are in either of the two sets. All simulations are performed under missing completely at random, missing at random and informatively missing mechanisms and for different missing data patterns and proportions. The proposal of a simple, novel, yet effective proposed procedure for training and testing using decision trees in the presence of missing data is the next contribution. Original and simple splitting criteria for attribute selection in tree building are put forward. The proposed technique is evaluated and validated in empirical tests over many real world application domains. In this work, the proposed algorithm maintains (sometimes exceeds) the outstanding accuracy of multiple imputation, especially on datasets containing mixed attributes and purely nominal attributes. Also, the proposed algorithm greatly improves in accuracy for IM data. Another major advantage of this method over multiple imputation is the important saving in computational resources due to it simplicity. The next contribution is the proposal of three versions of simple probabilistic techniques that could be used for classifying incomplete vectors using decision trees based on complete data. The proposed procedure is superficially similar to that of fractional cases but more effective. The experimental results demonstrate that these approaches can achieve comparative quality to sophisticated algorithms like multiple imputation and therefore are applicable to all kinds of datasets. Finally, novel uses of two proposed ensemble procedures for handling incomplete training and test data are proposed and discussed. The algorithms combine the two best approaches either with resampling (REMIMIA) or without resampling (EMIMIA) of the training data before growing the decision trees. Experiments are used to evaluate and validate the success of the proposed ensemble methods with respect to individual missing data techniques in the form of empirical tests. EMIMIA attains the highest overall level of prediction accuracy

The effect of missing values using genetic programming on evolvable diagnosis

Medical databases usually contain missing values due the policy of reducing stress and harm to the patient. In practice missing values has been a problem mainly due to the necessity to evaluate mathematical equations obtained by genetic programming. The solution to this problem is to use fill in methods to estimate the missing values. This paper analyses three fill in methods: (1) attribute means, (2) conditional means, and (3) random number generation. The methods are evaluated using sensitivity, specificity, and entropy to explain the exchange in knowledge of the results. The results are illustrated based on the breast cancer database. Conditional means produced the best fill in experimental results

A systematic review of data quality issues in knowledge discovery tasks

Hay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafío mas fundamental es la exploración de los grandes volúmenes de datos y la extracción de conocimiento útil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisión sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrícola conocida como la roya del café.Large volume of data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust

Link-Prediction Enhanced Consensus Clustering for Complex Networks

Many real networks that are inferred or collected from data are incomplete due to missing edges. Missing edges can be inherent to the dataset (Facebook friend links will never be complete) or the result of sampling (one may only have access to a portion of the data). The consequence is that downstream analyses that consume the network will often yield less accurate results than if the edges were complete. Community detection algorithms, in particular, often suffer when critical intra-community edges are missing. We propose a novel consensus clustering algorithm to enhance community detection on incomplete networks. Our framework utilizes existing community detection algorithms that process networks imputed by our link prediction based algorithm. The framework then merges their multiple outputs into a final consensus output. On average our method boosts performance of existing algorithms by 7% on artificial data and 17% on ego networks collected from Facebook

Missing Value Imputation With Unsupervised Backpropagation

Many data mining and data analysis techniques operate on dense matrices or complete tables of data. Real-world data sets, however, often contain unknown values. Even many classification algorithms that are designed to operate with missing values still exhibit deteriorated accuracy. One approach to handling missing values is to fill in (impute) the missing values. In this paper, we present a technique for unsupervised learning called Unsupervised Backpropagation (UBP), which trains a multi-layer perceptron to fit to the manifold sampled by a set of observed point-vectors. We evaluate UBP with the task of imputing missing values in datasets, and show that UBP is able to predict missing values with significantly lower sum-squared error than other collaborative filtering and imputation techniques. We also demonstrate with 24 datasets and 9 supervised learning algorithms that classification accuracy is usually higher when randomly-withheld values are imputed using UBP, rather than with other methods