Brute-force Missing Data Extreme Learning Machine for Predicting Huntington's Disease

An Extreme Learning Machine (ELM) randomly chooses hidden neurons and analytically determines the output weights (Huang, et al., 2005, 2006, 2008). With the ELM algorithm, only the connection weights between hidden layer and output layer are adjusted. The ELM algorithm tends to generalize better at a very fast learning speed: it can learn thousands of times faster than conventionally popular learning algorithms (Huang, et al., 2006). Artificial Neural Networks (ANNs) have been widely used as powerful information processing models and adopted in applications such as bankruptcy prediction, predicting costs, forecasting revenue, forecasting share prices and exchange rates, processing documents, and many more. Higher Order Neural Networks (HONNs) are ANNs in which the net input to a computational neuron is a weighted sum of products of its inputs. Real life data are not usually perfect. They contain wrong, incomplete, or vague data. Hence, it is usual to find missing data in many information sources used. Missing data is a common problem in statistical analysis (Little & Rubin, 1987). This chapter uses the Extreme Learning Machine (ELM) algorithm for HONN models and applies it in several significant business cases, which involve missing datasets. The experimental results demonstrate that HONN models with the ELM algorithm offer significant advantages over standard HONN models, such as faster training, as well as improved generalization abilities.

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Extreme learning machine for missing data using multiple imputations

Neurocomputing ◽

10.1016/j.neucom.2015.03.108 ◽

2016 ◽

Vol 174 ◽

pp. 220-231 ◽

Cited By ~ 52

Author(s):

Dušan Sovilj ◽

Emil Eirola ◽

Yoan Miche ◽

Kaj-Mikael Björk ◽

Rui Nian ◽

...

Keyword(s):

Missing Data ◽

Extreme Learning Machine ◽

Multiple Imputations ◽

Learning Machine

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Regularized extreme learning machine for regression with missing data

Neurocomputing ◽

10.1016/j.neucom.2012.02.040 ◽

2013 ◽

Vol 102 ◽

pp. 45-51 ◽

Cited By ~ 77

Author(s):

Qi Yu ◽

Yoan Miche ◽

Emil Eirola ◽

Mark van Heeswijk ◽

Eric Séverin ◽

...

Keyword(s):

Missing Data ◽

Extreme Learning Machine ◽

Learning Machine

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Exploiting Earth Observation Data to Impute Groundwater Level Measurements with an Extreme Learning Machine

Remote Sensing ◽

10.3390/rs12122044 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2044

Author(s):

Steven Evans ◽

Gustavious P. Williams ◽

Norman L. Jones ◽

Daniel P. Ames ◽

E. James Nelson

Keyword(s):

Soil Moisture ◽

Missing Data ◽

Extreme Learning Machine ◽

Groundwater Resources ◽

Earth Observation ◽

Imputation Method ◽

Observation Data ◽

Monitoring Wells ◽

Earth Observation Data ◽

Learning Machine

Groundwater resources are expensive to develop and use; they are difficult to monitor and data collected from monitoring wells are often sporadic, often only available at irregular, infrequent, or brief intervals. Groundwater managers require an accurate understanding of historic groundwater storage trends to effectively manage groundwater resources, however, most if not all well records contain periods of missing data. To understand long-term trends, these missing data need to be imputed before trend analysis. We present a method to impute missing data at single wells, by exploiting data generated from Earth observations that are available globally. We use two soil moisture models, the Global Land Data Assimilation System (GLDAS) model and National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) soil moisture model to impute the missing data. Our imputation method uses a machine learning technique called Extreme Learning Machine (ELM). Our implementation uses 11 input data-streams, all based on Earth observation data. We train and apply the model one well at a time. We selected ELM because it is a single hidden layer feedforward model that can be trained quickly on minimal data. We tested the ELM method using data from monitoring wells in the Cedar Valley and Beryl-Enterprise areas in southwest Utah, USA. We compute error estimates for the imputed data and show that ELM-computed estimates were more accurate than Kriging estimates. This ELM-based data imputation method can be used to impute missing data at wells. These complete time series can be used improve the accuracy of aquifer groundwater elevation maps in areas where in-situ well measurements are sparse, resulting in more accurate spatial estimates of the groundwater surface. The data we use are available globally from 1950 to the present, so this method can be used anywhere in the world.

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An Imputation Method for Missing Data Based on an Extreme Learning Machine Auto-Encoder

IEEE Access ◽

10.1109/access.2018.2868729 ◽

2018 ◽

Vol 6 ◽

pp. 52930-52935 ◽

Cited By ~ 5

Author(s):

Cheng-Bo Lu ◽

Ying Mei

Keyword(s):

Missing Data ◽

Extreme Learning Machine ◽

Imputation Method ◽

Learning Machine

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Sample-Based Extreme Learning Machine with Missing Data

Mathematical Problems in Engineering ◽

10.1155/2015/145156 ◽

2015 ◽

Vol 2015 ◽

pp. 1-11 ◽

Cited By ~ 3

Author(s):

Hang Gao ◽

Xin-Wang Liu ◽

Yu-Xing Peng ◽

Song-Lei Jian

Keyword(s):

Missing Data ◽

Learning Community ◽

Extreme Learning Machine ◽

Missing Values ◽

High Efficiency ◽

Synthetic Data ◽

Data Sets ◽

Data Set ◽

Practical Applications ◽

Learning Machine

Extreme learning machine (ELM) has been extensively studied in machine learning community during the last few decades due to its high efficiency and the unification of classification, regression, and so forth. Though bearing such merits, existing ELM algorithms cannot efficiently handle the issue of missing data, which is relatively common in practical applications. The problem of missing data is commonly handled by imputation (i.e., replacing missing values with substituted values according to available information). However, imputation methods are not always effective. In this paper, we propose a sample-based learning framework to address this issue. Based on this framework, we develop two sample-based ELM algorithms for classification and regression, respectively. Comprehensive experiments have been conducted in synthetic data sets, UCI benchmark data sets, and a real world fingerprint image data set. As indicated, without introducing extra computational complexity, the proposed algorithms do more accurate and stable learning than other state-of-the-art ones, especially in the case of higher missing ratio.

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