This review paper provides an overview of data pre-processing in Machine learning, focusing on all types of problems while building the machine learning problems. It deals with two significant issues in the pre-processing process (i). issues with data and (ii). Steps to follow to do data analysis with its best approach. As raw data are vulnerable to noise, corruption, missing, and inconsistent data, it is necessary to perform pre-processing steps, which is done using classification, clustering, and association and many other pre-processing techniques available. Poor data can primarily affect the accuracy and lead to false prediction, so it is necessary to improve the dataset's quality. So, data pre-processing is the best way to deal with such problems. It makes the knowledge extraction from the data set much easier with cleaning, Integration, transformation, and reduction methods. The issue with Data missing and significant differences in the variety of data always exists as the information is collected through multiple sources and from a real-world application. So, the data augmentation approach generates data for machine learning models. To decrease the dependency on training data and to improve the performance of the machine learning model. This paper discusses flipping, rotating with slight degrees and others to augment the image data and shows how to perform data augmentation methods without distorting the original data. 

A Review: Data Pre-Processing and Data Augmentation Techniques

The core objective of this study is to carry out rainfall forecasting over the Langat River Basin through the integration of wavelet transform (WT) and convolutional neural network (CNN). The proposed method involves using CNN for feature extraction to efficiently learn from the raw rainfall dataset. With the aid of deep architecture, a highly abstracted representation of the inputs time series with a high level of interpretation is formed at each subsequent CNN layer. The use of WT in forecasting the rainfall time series is by preprocessing the raw rainfall dataset into a set of decomposed wavelet components as inputs for the CNN model using discrete wavelet transform (DWT). The conditions for discretizing the raw input through DWT are discussed, along with the criteria to be used. Daily datasets, ranging from January 2002 to December 2017, were used. The results showed that the proposed model could satisfactorily capture patterns of the rainfall time series, for both monthly rainfalls forecasting or daily rainfall forecasting. Three performance indices were used to evaluate the model accuracy: RMSE, RSR, and MAE. These statistical indices have a range of value from 0 to a finite value that depends on the scale of the number used. In general, a lower value is better than a higher one.

Performance Enhancement Model for Rainfall Forecasting Utilizing Integrated Wavelet-Convolutional Neural Network

Wind energy has emerged as a highly promising source of renewable energy in recent times. However, wind turbines regularly suffer from operational inconsistencies, leading to significant costs and challenges in operations and maintenance (O&M). Condition-based monitoring (CBM) and performance assessment/analysis of turbines are vital aspects for ensuring efficient O&M planning and cost minimisation. Data-driven decision making techniques have witnessed rapid evolution in the wind industry for such O&M tasks during the last decade, from applying signal processing methods in early 2010 to artificial intelligence (AI) techniques, especially deep learning in 2020. In this article, we utilise statistical computing to present a scientometric review of the conceptual and thematic evolution of AI in the wind energy sector, providing evidence-based insights into present strengths and limitations of data-driven decision making in the wind industry. We provide a perspective into the future and on current key challenges in data availability and quality, lack of transparency in black box-natured AI models, and prevailing issues in deploying models for real-time decision support, along with possible strategies to overcome these problems. We hope that a systematic analysis of the past, present and future of CBM and performance assessment can encourage more organisations to adopt data-driven decision making techniques in O&M towards making wind energy sources more reliable, contributing to the global efforts of tackling climate change.

Scientometric review of artificial intelligence for operations & maintenance of wind turbines: The past, present and future

The superior multi-functional properties of polymer composites have made them an ideal choice for aerospace, automobile, marine, civil, and many other technologically demanding industries. The increasing demand of these composites calls for an extensive investigation of their physical, chemical and mechanical behavior under different exposure conditions. Machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modeling, leading to unprecedented insights and exploration of the system properties beyond the capability of traditional computational and experimental analyses. Here we aim to abridge the findings of the large volume of relevant literature and highlight the broad spectrum potential of ML in applications like prediction, optimization, feature identification, uncertainty quantification, reliability and sensitivity analysis along with the framework of different ML algorithms concerning polymer composites. Challenges like the curse of dimensionality, overfitting, noise and mixed variable problems are discussed, including the latest advancements in ML that have the potential to be integrated in the field of polymer composites. Based on the extensive literature survey, a few recommendations on the exploitation of various ML algorithms for addressing different critical problems concerning polymer composites are provided along with insightful perspectives on the potential directions of future research. 

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Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Undersampling is a widely used resampling technique for imbalanced data. As traditional undersampling techniques, typically making majority and minority classes in imbalanced data into the same scale, tend to miss valuable information, many strategies like clustering have been developed. However, two essential problems still remain and require more efforts to be put; that is, which and how many instances should be extracted in undersampling. To alleviate these two problems, in this paper we propose a novel undersampling method for imbalanced data. It exploits a sequence of density peaks to progressively extract instances from the majority classes of the imbalanced data. Specifically, two factors are introduced to measure the importance degree of each instance in the majority classes. With these two factors, we generate a sampling sequence based on the importance of instances for classification. Furthermore, the optimal undersampling size of the majority classes is automatically determined by progressively extracting the important instances from the sequence. To evaluate the effectiveness of the proposed method, a series of experiments comparing to six popular undersampling methods were conducted on 40 public benchmark datasets. The experimental results show that the performance of the proposed undersampling method is superior to the state-of-the-art undersampling methods.

A novel progressively undersampling method based on the density peaks sequence for imbalanced data

Dealing with noise problem in machine learning data-sets: A systematic review

Automated machine classification will play a vital role in the machine learning and data mining. It is probable that each classifier will work well on some data sets and not so well in others, increasing the evaluation significance. The performance of the learning models will intensely rely on upon the characteristics of the data sets. The previous outcomes recommend that overlapping between classes and the presence of noise has the most grounded impact on the performance of learning algorithm. The class overlap problem is a critical problem in which data samples appear as valid instances of more than one class which may be responsible for the presence of noise in data sets.The objective of this paper is to comprehend better the data used as a part of machine learning problems so as to learn issues and to analyze the instances that are profoundly covered by utilizing new proposed overlap measures. The proposed overlap measures are Nearest Enemy Ratio, SubConcept Ratio, Likelihood Ratio and Soft Margin Ratio. To perform this experiment, we have created 438 binary classification data sets from real-world problems and computed the value of 12 data complexity metrics to find highly overlapped data sets. After that we apply measures to identify the overlapped instances and four noise filters to find the noisy instances. From results, we found that 60–80% overlapped instances are noisy instances in data sets by using four noise filters. We found that class overlap is a principal contributor to introduce class noise in data sets.

Handling Class Overlapping to Detect Noisy Instances in Classification

The performance of the learning models will intensely rely on the characteristics of the training data. The previous outcomes recommend that the overlapping between classes and the presence of noise have the most grounded impact on the performance of learning algorithm, and software defect datasets are no exceptions. The class overlap problem is concerned with the performance of machine learning classifiers critical problem is class overlap in which data samples appear as valid examples of more than one class which may be responsible for the presence of noise in datasets. We aim to investigate how the presence of overlapped instances in a dataset influences the classifier’s performance, and how to deal with class overlapping problem. To have a close estimate of class overlapping, we have proposed four different measures namely, nearest enemy ratio, subconcept ratio, likelihood ratio and soft margin ratio. We performed our investigations using 327 binary defect classification datasets obtained from 54 software projects, where we first identified overlapped datasets using three data complexity measures proposed in the literature. We also include treatment effort into the prediction process. Subsequently, we used our proposed measures to find overlapped instances in the identified overlapped datasets. Our results indicated that by training a classifier on a training data free from overlapped instances led to an improved classifier performance on the test data containing overlapped instances. The classifiers perform significantly better when the evaluation measure takes the effort into account.

A set of measures designed to identify overlapped instances in software defect prediction

A classifier’s performance can be greatly influenced by the characteristics of the underlying dataset We aim at investigating the connection between the overlapping complexity of dataset and the performance of a classifier in order to understand the domain of competence of these machine learning classifiers In this paper, we report the results and implications of a study investigating the connection between four overlapping measures and the performance of three classifiers, namely KNN, C45 and SVM In this study, we first evaluated the performance of the three classifiers over 1060 binary classification datasets Next, we constructed a multi-label classification dataset by computing the four overlapping measures as features and multi-labeled with the competent classifiers over these 1060 binary classification datasets The generated multi-label classification dataset is then used to estimate the domain of the competence of the three classifiers with respect to the overlapping complexity This allowed us to express the domain of competence of these classifiers as a set of rules obtained through multi-label rule learning We found classifiers’ performance invariably degraded with the datasets having high values of complexity measures (N1 and N3) This suggested for the existence of a strong negative correlation between the classifiers’ performance and class overlapping present in the data

Domain of Competency of Classifiers on Overlapping Complexity of Datasets Using Multi-label Classification with Meta-Learning

Many routing protocols are proposed and used for wireless sensor networks that use clustering for load sharing in the network. This is a significant technique which extends the network lifetime. Dynamic or topology makes cluster head selection a difficult task in the network. The following paper proposed a fuzzy logic-based cluster head selection method and compared with LEACH routing protocol which considered as the benchmark algorithm for the cluster-based routing algorithm for WSN.

Shivani Gupta

Papers

Dealing with noise problem in machine learning data-sets: A systematic review

Handling Class Overlapping to Detect Noisy Instances in Classification

A set of measures designed to identify overlapped instances in software defect prediction

Domain of Competency of Classifiers on Overlapping Complexity of Datasets Using Multi-label Classification with Meta-Learning

Energy Efficient Cluster Head Selection for Wireless Sensor Network Using Fuzzy Logic