scholarly journals ENHANCING THE CLASSIFICATION ACCURACY OF NOISY DATASET BY FUSING CORRELATION BASED FEATURE SELECTION WITH K-NEAREST NEIGHBOUR

2017 ◽  
Vol 10 (2) ◽  
pp. 282-290
Author(s):  
Samir Singha ◽  
Syed Hassan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Missing Values ◽  
Noisy Data ◽  
Nearest Neighbour ◽  
Prediction Ability ◽  
Correlation Based Feature Selection

The performance of data mining and machine learning tasks can be significantly degraded due to the presence of noisy, irrelevant and high dimensional data containing large number of features. A large amount of real world data consist of noise or missing values. While collecting data, there may be many irrelevant features that are collected by the storage repositories. These redundant and irrelevant feature values distorts the classification principle and simultaneously increases calculations overhead and decreases the prediction ability of the classifier. The high-dimensionality of such datasets possesses major bottleneck in the field of data mining, statistics, machine learning. Among several methods of dimensionality reduction, attribute or feature selection technique is often used in dimensionality reduction. Since the k-NN algorithm is sensitive to irrelevant attributes therefore its performance degrades significantly when a dataset contains missing values or noisy data. However, this weakness of the k-NN algorithm can be minimized when combined with the other feature selection techniques. In this research we combine the Correlation based Feature Selection (CFS) with k-Nearest Neighbour (k-NN) Classification algorithm to find better result in classification when the dataset contains missing values or noisy data. The reduced attribute set decreases the time required for classification. The research shows that when dimensionality reduction is done using CFS and classified with k-NN algorithm, dataset with nil or very less noise may have negative impact in the classification accuracy, when compared with classification accuracy of k-NN algorithm alone. When additional noise is introduced to these datasets, the performance of k-NN degrades significantly. When these noisy datasets are classified using CFS and k-NN together, the percentage in classification accuracy is improved.

scholarly journals A Hybrid Feature Selection Method for Improve the Accuracy of Medical Classification Process

2021 ◽  
Vol 11 (1) ◽  
pp. 50-55
Author(s):  
Maria Mohammad Yousef ◽  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Classification Accuracy ◽  
Fitness Function ◽  
Machine Learning Algorithms ◽  
Feature Subset Selection ◽  
High Dimensionality ◽  
Support Vector ◽  
Feature Subset

Generally, medical dataset classification has become one of the biggest problems in data mining research. Every database has a given number of features but it is observed that some of these features can be redundant and can be harmful as well as disrupt the process of classification and this problem is known as a high dimensionality problem. Dimensionality reduction in data preprocessing is critical for increasing the performance of machine learning algorithms. Besides the contribution of feature subset selection in dimensionality reduction gives a significant improvement in classification accuracy. In this paper, we proposed a new hybrid feature selection approach based on (GA assisted by KNN) to deal with issues of high dimensionality in biomedical data classification. The proposed method first applies the combination between GA and KNN for feature selection to find the optimal subset of features where the classification accuracy of the k-Nearest Neighbor (kNN) method is used as the fitness function for GA. After selecting the best-suggested subset of features, Support Vector Machine (SVM) are used as the classifiers. The proposed method experiments on five medical datasets of the UCI Machine Learning Repository. It is noted that the suggested technique performs admirably on these databases, achieving higher classification accuracy while using fewer features.

scholarly journals Effect of Feature Selection on Gene Expression Datasets Classification Accurac

2018 ◽  
Vol 8 (5) ◽  
pp. 3194 ◽  
Author(s):  
Hicham Omara ◽  
Mohamed Lazaar ◽  
Youness Tabii
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Classification Accuracy ◽  
Selection Methods ◽  
Dimentionality Reduction

<span>Feature selection attracts researchers who deal with machine learning and data mining. It consists of selecting the variables that have the greatest impact on the dataset classification, and discarding the rest. This dimentionality reduction allows classifiers to be fast and more accurate. This paper traits the effect of feature selection on the accuracy of widely used classifiers in literature. These classifiers are compared with three real datasets which are pre-processed with feature selection methods. More than 9% amelioration in classification accuracy is observed, and k-means appears to be the most sensitive classifier to feature selection.</span>

Data Mining-based Financial Statement Fraud Detection: Systematic Literature Review and Meta-analysis to Estimate Data Sample Mapping of Fraudulent Companies Against Non-fraudulent Companies

2021 ◽  
pp. 097215092098485
Author(s):  
Sonika Gupta ◽  
Sushil Kumar Mehta
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Classification Accuracy ◽  
Meta Analysis ◽  
Financial Statement ◽  
Research Articles ◽  
Financial Statement Fraud ◽  
Data Mining Techniques

Data mining techniques have proven quite effective not only in detecting financial statement frauds but also in discovering other financial crimes, such as credit card frauds, loan and security frauds, corporate frauds, bank and insurance frauds, etc. Classification of data mining techniques, in recent years, has been accepted as one of the most credible methodologies for the detection of symptoms of financial statement frauds through scanning the published financial statements of companies. The retrieved literature that has used data mining classification techniques can be broadly categorized on the basis of the type of technique applied, as statistical techniques and machine learning techniques. The biggest challenge in executing the classification process using data mining techniques lies in collecting the data sample of fraudulent companies and mapping the sample of fraudulent companies against non-fraudulent companies. In this article, a systematic literature review (SLR) of studies from the area of financial statement fraud detection has been conducted. The review has considered research articles published between 1995 and 2020. Further, a meta-analysis has been performed to establish the effect of data sample mapping of fraudulent companies against non-fraudulent companies on the classification methods through comparing the overall classification accuracy reported in the literature. The retrieved literature indicates that a fraudulent sample can either be equally paired with non-fraudulent sample (1:1 data mapping) or be unequally mapped using 1:many ratio to increase the sample size proportionally. Based on the meta-analysis of the research articles, it can be concluded that machine learning approaches, in comparison to statistical approaches, can achieve better classification accuracy, particularly when the availability of sample data is low. High classification accuracy can be obtained with even a 1:1 mapping data set using machine learning classification approaches.

scholarly journals Dimension Reduction for Objects Composed of Vector Sets

2017 ◽  
Vol 27 (1) ◽  
pp. 169-180 ◽  
Author(s):  
Marton Szemenyei ◽  
Ferenc Vajda
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Discriminant Analysis ◽  
Probability Distribution ◽  
Dimension Reduction ◽  
Pose Estimation ◽  
Real World ◽  
Single Object ◽  
Real World Datasets

Abstract Dimension reduction and feature selection are fundamental tools for machine learning and data mining. Most existing methods, however, assume that objects are represented by a single vectorial descriptor. In reality, some description methods assign unordered sets or graphs of vectors to a single object, where each vector is assumed to have the same number of dimensions, but is drawn from a different probability distribution. Moreover, some applications (such as pose estimation) may require the recognition of individual vectors (nodes) of an object. In such cases it is essential that the nodes within a single object remain distinguishable after dimension reduction. In this paper we propose new discriminant analysis methods that are able to satisfy two criteria at the same time: separating between classes and between the nodes of an object instance. We analyze and evaluate our methods on several different synthetic and real-world datasets.

scholarly journals A Contemporary Machine Learning Method for Accurate Prediction of Cervical Cancer

2021 ◽  
Vol 102 ◽  
pp. 04004
Author(s):  
Jesse Jeremiah Tanimu ◽  
Mohamed Hamada ◽  
Mohammed Hassan ◽  
Saratu Yusuf Ilu
Keyword(s):  
Machine Learning ◽  
Cervical Cancer ◽  
Feature Selection ◽  
Decision Tree ◽  
Sensitivity And Specificity ◽  
Missing Values ◽  
New Technologies ◽  
Machine Learning Techniques ◽  
Screening Tests ◽  
Tree Classifier

With the advent of new technologies in the medical field, huge amounts of cancerous data have been collected and are readily accessible to the medical research community. Over the years, researchers have employed advanced data mining and machine learning techniques to develop better models that can analyze datasets to extract the conceived patterns, ideas, and hidden knowledge. The mined information can be used as a support in decision making for diagnostic processes. These techniques, while being able to predict future outcomes of certain diseases effectively, can discover and identify patterns and relationships between them from complex datasets. In this research, a predictive model for predicting the outcome of patients’ cervical cancer results has been developed, given risk patterns from individual medical records and preliminary screening tests. This work presents a Decision tree (DT) classification algorithm and shows the advantage of feature selection approaches in the prediction of cervical cancer using recursive feature elimination technique for dimensionality reduction for improving the accuracy, sensitivity, and specificity of the model. The dataset employed here suffers from missing values and is highly imbalanced. Therefore, a combination of under and oversampling techniques called SMOTETomek was employed. A comparative analysis of the proposed model has been performed to show the effectiveness of feature selection and class imbalance based on the classifier’s accuracy, sensitivity, and specificity. The DT with the selected features and SMOTETomek has better results with an accuracy of 98%, sensitivity of 100%, and specificity of 97%. Decision Tree classifier is shown to have excellent performance in handling classification assignment when the features are reduced, and the problem of imbalance class is addressed.

scholarly journals Zero-Shot Feature Selection via Transferring Supervised Knowledge

2021 ◽  
Vol 17 (2) ◽  
pp. 1-20
Author(s):  
Zheng Wang ◽  
Qiao Wang ◽  
Tingzhang Zhao ◽  
Chaokun Wang ◽  
Xiaojun Ye
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Real World ◽  
Rapid Growth ◽  
Learning Systems ◽  
Training Data ◽  
Effective Technique ◽  
Supervised Methods ◽  
Real World Datasets

Feature selection, an effective technique for dimensionality reduction, plays an important role in many machine learning systems. Supervised knowledge can significantly improve the performance. However, faced with the rapid growth of newly emerging concepts, existing supervised methods might easily suffer from the scarcity and validity of labeled data for training. In this paper, the authors study the problem of zero-shot feature selection (i.e., building a feature selection model that generalizes well to “unseen” concepts with limited training data of “seen” concepts). Specifically, they adopt class-semantic descriptions (i.e., attributes) as supervision for feature selection, so as to utilize the supervised knowledge transferred from the seen concepts. For more reliable discriminative features, they further propose the center-characteristic loss which encourages the selected features to capture the central characteristics of seen concepts. Extensive experiments conducted on various real-world datasets demonstrate the effectiveness of the method.

scholarly journals Evolutionary Machine Learning for Classification with Incomplete Data

2021 ◽  
Author(s):  
◽  
Cao Truong Tran
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Genetic Programming ◽  
Incomplete Data ◽  
Missing Values ◽  
Machine Learning Techniques ◽  
Feature Construction ◽  
Classification Algorithms ◽  
Learning Techniques ◽  
Effectiveness And Efficiency

<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>

scholarly journals A Comparison of Machine Learning Methods in a High-Dimensional Classification Problem

2014 ◽  
Vol 5 (3) ◽  
pp. 82-96 ◽  
Author(s):  
Marijana Zekić-Sušac ◽  
Sanja Pfeifer ◽  
Nataša Šarlija
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Classification Accuracy ◽  
Classification Problem ◽  
High Dimensional ◽  
Nearest Neighbour ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Dimensional Classification ◽  
Artificial Neural

Abstract Background: Large-dimensional data modelling often relies on variable reduction methods in the pre-processing and in the post-processing stage. However, such a reduction usually provides less information and yields a lower accuracy of the model. Objectives: The aim of this paper is to assess the high-dimensional classification problem of recognizing entrepreneurial intentions of students by machine learning methods. Methods/Approach: Four methods were tested: artificial neural networks, CART classification trees, support vector machines, and k-nearest neighbour on the same dataset in order to compare their efficiency in the sense of classification accuracy. The performance of each method was compared on ten subsamples in a 10-fold cross-validation procedure in order to assess computing sensitivity and specificity of each model. Results: The artificial neural network model based on multilayer perceptron yielded a higher classification rate than the models produced by other methods. The pairwise t-test showed a statistical significance between the artificial neural network and the k-nearest neighbour model, while the difference among other methods was not statistically significant. Conclusions: Tested machine learning methods are able to learn fast and achieve high classification accuracy. However, further advancement can be assured by testing a few additional methodological refinements in machine learning methods.

Performance Analysis of Classifiers on Filter-Based Feature Selection Approaches on Microarray Data

2017 ◽  
pp. 41-70 ◽  
Author(s):  
Arunkumar Chinnaswamy ◽  
Ramakrishnan Srinivasan
Keyword(s):  
Feature Selection ◽  
Microarray Data ◽  
Classification Accuracy ◽  
Information Gain ◽  
Feature Subset ◽  
Classification Problems ◽  
Raw Data ◽  
Correlation Based Feature Selection ◽  
Feature Selection Approach ◽  
Gene Expression Levels

The process of Feature selection in machine learning involves the reduction in the number of features (genes) and similar activities that results in an acceptable level of classification accuracy. This paper discusses the filter based feature selection methods such as Information Gain and Correlation coefficient. After the process of feature selection is performed, the selected genes are subjected to five classification problems such as Naïve Bayes, Bagging, Random Forest, J48 and Decision Stump. The same experiment is performed on the raw data as well. Experimental results show that the filter based approaches reduce the number of gene expression levels effectively and thereby has a reduced feature subset that produces higher classification accuracy compared to the same experiment performed on the raw data. Also Correlation Based Feature Selection uses very fewer genes and produces higher accuracy compared to Information Gain based Feature Selection approach.

A NOVEL FEATURE SELECTION ALGORITHM WITH SUPERVISED MUTUAL INFORMATION FOR CLASSIFICATION

2013 ◽  
Vol 22 (04) ◽  
pp. 1350027
Author(s):  
JAGANATHAN PALANICHAMY ◽  
KUPPUCHAMY RAMASAMY
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Feature Selection ◽  
Mutual Information ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Class A ◽  
Selection Algorithms ◽  
The Relationship ◽  
Class Variable

Feature selection is essential in data mining and pattern recognition, especially for database classification. During past years, several feature selection algorithms have been proposed to measure the relevance of various features to each class. A suitable feature selection algorithm normally maximizes the relevancy and minimizes the redundancy of the selected features. The mutual information measure can successfully estimate the dependency of features on the entire sampling space, but it cannot exactly represent the redundancies among features. In this paper, a novel feature selection algorithm is proposed based on maximum relevance and minimum redundancy criterion. The mutual information is used to measure the relevancy of each feature with class variable and calculate the redundancy by utilizing the relationship between candidate features, selected features and class variables. The effectiveness is tested with ten benchmarked datasets available in UCI Machine Learning Repository. The experimental results show better performance when compared with some existing algorithms.

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