scholarly journals A Novel Hybrid Approach for Chronic Disease Classification

2020 ◽  
Vol 15 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Divya Jain ◽  
Vijendra Singh
Keyword(s):  
Chronic Disease ◽  
Classification Accuracy ◽  
Hybrid Approach ◽  
Substantial Reduction ◽  
Disease Classification ◽  
Computational Time ◽  
Second Phase ◽  
Two Phase ◽  
Hybrid Classification ◽  
Diagnostic Framework

A two-phase diagnostic framework based on hybrid classification for the diagnosis of chronic disease is proposed. In the first phase, feature selection via ReliefF method and feature extraction via PCA method are incorporated. In the second phase, efficient optimization of SVM parameters via grid search method is performed. The proposed hybrid classification approach is then tested with seven popular chronic disease datasets using a cross-validation method. Experiments are then conducted to evaluate the presented classification method vis-à-vis four other existing classifiers that are applied on the same chronic disease datasets. Results show that the presented approach reduces approximately 40% of the extraneous and surplus features with substantial reduction in the execution time for mining all datasets, achieving the highest classification accuracy of 98.5%. It is concluded that with the presented approach, excellent classification accuracy is achieved for each chronic disease dataset while irrelevant and redundant features may be eliminated, thereby substantially reducing the diagnostic complexity and resulting computational time.

Dimensionality Reduction with Unsupervised Feature Selection and Applying Non-Euclidean Norms for Classification Accuracy

2011 ◽  
pp. 91-109
Author(s):  
Amit Saxena ◽  
John Wang
Keyword(s):  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Fitness Function ◽  
Synthetic Data ◽  
Feature Subset Selection ◽  
Second Phase ◽  
Data Sets ◽  
Feature Subset ◽  
K Nearest Neighbor ◽  
Two Phase

This paper presents a two-phase scheme to select reduced number of features from a dataset using Genetic Algorithm (GA) and testing the classification accuracy (CA) of the dataset with the reduced feature set. In the first phase of the proposed work, an unsupervised approach to select a subset of features is applied. GA is used to select stochastically reduced number of features with Sammon Error as the fitness function. Different subsets of features are obtained. In the second phase, each of the reduced features set is applied to test the CA of the dataset. The CA of a data set is validated using supervised k-nearest neighbor (k-nn) algorithm. The novelty of the proposed scheme is that each reduced feature set obtained in the first phase is investigated for CA using the k-nn classification with different Minkowski metric i.e. non-Euclidean norms instead of conventional Euclidean norm (L2). Final results are presented in the paper with extensive simulations on seven real and one synthetic, data sets. It is revealed from the proposed investigation that taking different norms produces better CA and hence a scope for better feature subset selection.

scholarly journals Integrating Anticipative Replenishment Allocation with Reactive Fulfillment for Online Retailing Using Robust Optimization

2020 ◽  
Author(s):  
Yun Fong Lim ◽  
Song Jiu ◽  
Marcus Ang
Keyword(s):  
Robust Optimization ◽  
Operating Cost ◽  
Planning Horizon ◽  
Problem Definition ◽  
Computational Time ◽  
Second Phase ◽  
Online Retailer ◽  
Two Phase ◽  
Solution Quality ◽  
Phase Approach

Problem definition: In each period of a planning horizon, an online retailer decides how much to replenish each product and how to allocate its inventory to fulfillment centers (FCs) before demand is known. After the demand in the period is realized, the retailer decides on which FCs to fulfill it. It is crucial to optimize the replenishment, allocation, and fulfillment decisions jointly such that the expected total operating cost is minimized. The problem is challenging because the replenishment allocation is done in an anticipative manner under a push strategy, but the fulfillment is executed in a reactive way under a pull strategy. We propose a multiperiod stochastic optimization model to delicately integrate the anticipative replenishment allocation decisions with the reactive fulfillment decisions such that they are determined seamlessly as the demands are realized over time. Academic/practical relevance: The aggressive expansion in e-commerce sales significantly escalates online retailers’ operating costs. Our methodology helps boost their competency in this cutthroat industry. Methodology: We develop a two-phase approach based on robust optimization to solve the problem. The first phase decides whether the products should be replenished in each period (binary decisions). We fix these binary decisions in the second phase, in which we determine the replenishment, allocation, and fulfillment quantities. Results: Numerical experiments suggest that our approach outperforms existing methods from the literature in solution quality and computational time and performs within 7% of a benchmark with perfect information. A study using real data from a major fashion online retailer in Asia suggests that the two-phase approach can potentially reduce the retailer’s cumulative cost significantly. Managerial implications: By decoupling the binary decisions from the continuous decisions, our methodology can solve large problem instances (up to 1,200 products). The integration, robustness, and adaptability of the decisions under our approach create significant value.

scholarly journals A hybrid approach for modelling multidimensional degradation processes

2021 ◽  
Vol 1207 (1) ◽  
pp. 012023
Author(s):  
Renyan Jiang ◽  
Binbin Xiong
Keyword(s):  
Model Selection ◽  
Residual Life ◽  
Hybrid Approach ◽  
Degradation Process ◽  
Closed Form Expression ◽  
Second Phase ◽  
Two Phase ◽  
Degradation Model ◽  
One Dimensional ◽  
Degradation Processes

Abstract Degradation processes are often multidimensional. Modeling such degradation processes needs to address two key issues: indicator fusion and degradation model selection; and they have been separately addressed in the literature. This paper proposes a hybrid approach to jointly address these two issues. The proposed approach first fuses multiple degradation indicators into a composite degradation indicator. This fusion step involves data normalization, aggregation model selection and determination of indicator weights. After the fusion step, the problem becomes one-dimensional, and the existing method to select the degradation model for a one-dimensional degradation process can be applied. The resulting model obtained from the proposed approach can be a two-phase model; and the model for the second phase has a closed-form expression. This considerably facilitates residual life prediction. A real-world example is included to illustrate the proposed approach and its appropriateness.

What make investors herd while investing in the Indian stock market? A hybrid approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Muskan Sachdeva ◽  
Ritu Lehal ◽  
Sanjay Gupta ◽  
Aashish Garg
Keyword(s):  
Stock Market ◽  
Fuzzy Ahp ◽  
Hybrid Approach ◽  
North India ◽  
Second Phase ◽  
Herd Behavior ◽  
Herding Behavior ◽  
Two Phase ◽  
Content Type ◽  
Indian Stock Market

PurposeIn recent years, significant research has focused on the question of whether severe market periods are accompanied by herding behavior. As herding behavior is a considerable cause of the speculative bubble and leads to stock market deviations from their basic values it is necessary to examine the motivators which led to herding behavior among investors. The paper aims to discuss this issue.Design/methodology/approachIn this study, the authors performed a two-phase analysis to address the research questions of the study. In the first phase, for text analysis NVivo software was used to identify the factors driving herding behavior among Indian stock investors. The analysis of a text was performed using word frequency analysis. While in the second phase, the Fuzzy-AHP analysis techniques were employed to examine the relative importance of all the factors determined and assign priorities to the factors extracted.FindingsResults of the study depicted Investor Cognitive Psychology (ICP), Market Information (MI), Stock Characteristics (SC) as the top-ranked factors driving herding behavior, while Socio-Economic Factors (SEF) emerged as the least important factor driving herding behavior.Research limitations/implicationsThe current study was undertaken among stock investors from North India only. Moreover, numerous factors are not part of the study but might significantly influence the investors' herding behaviors.Practical implicationsComprehending the influences of the different factors discussed in the study would enable stock investors to be more aware of their investment choices and not resort to herd behavior. This research enables decision-makers to understand the reasons for herd activity and helps them act accordingly to improve the stock market's performance.Originality/valueThe current study will provide an inclusive overview of herding behavior motivators among Indian stock investors. This study's results can be extremely useful for both academics and policymakers to gain some insight into the functioning of the Indian stock market.

Dimensionality Reduction with Unsupervised Feature Selection and Applying Non-Euclidean Norms for Classification Accuracy

2010 ◽  
Vol 6 (2) ◽  
pp. 22-40 ◽  
Author(s):  
Amit Saxena ◽  
John Wang
Keyword(s):  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Fitness Function ◽  
Feature Subset Selection ◽  
Second Phase ◽  
Feature Subset ◽  
K Nearest Neighbor ◽  
Two Phase ◽  
Data Set ◽  
Minkowski Metric

This paper presents a two-phase scheme to select reduced number of features from a dataset using Genetic Algorithm (GA) and testing the classification accuracy (CA) of the dataset with the reduced feature set. In the first phase of the proposed work, an unsupervised approach to select a subset of features is applied. GA is used to select stochastically reduced number of features with Sammon Error as the fitness function. Different subsets of features are obtained. In the second phase, each of the reduced features set is applied to test the CA of the dataset. The CA of a data set is validated using supervised k-nearest neighbor (k-nn) algorithm. The novelty of the proposed scheme is that each reduced feature set obtained in the first phase is investigated for CA using the k-nn classification with different Minkowski metric i.e. non-Euclidean norms instead of conventional Euclidean norm (L2). Final results are presented in the paper with extensive simulations on seven real and one synthetic, data sets. It is revealed from the proposed investigation that taking different norms produces better CA and hence a scope for better feature subset selection.

scholarly journals A Two-Phase Approach for Semi-Supervised Feature Selection

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 215
Author(s):  
Amit Saxena ◽  
Shreya Pare ◽  
Mahendra Singh Meena ◽  
Deepak Gupta ◽  
Akshansh Gupta ◽  
...  
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Second Phase ◽  
Two Phase ◽  
Phase Approach ◽  
Whole Process ◽  
Novel Approach ◽  
Two Phases ◽  
Number Of Classes ◽  
The Given

This paper proposes a novel approach for selecting a subset of features in semi-supervised datasets where only some of the patterns are labeled. The whole process is completed in two phases. In the first phase, i.e., Phase-I, the whole dataset is divided into two parts: The first part, which contains labeled patterns, and the second part, which contains unlabeled patterns. In the first part, a small number of features are identified using well-known maximum relevance (from first part) and minimum redundancy (whole dataset) based feature selection approaches using the correlation coefficient. The subset of features from the identified set of features, which produces a high classification accuracy using any supervised classifier from labeled patterns, is selected for later processing. In the second phase, i.e., Phase-II, the patterns belonging to the first and second part are clustered separately into the available number of classes of the dataset. In the clusters of the first part, take the majority of patterns belonging to a cluster as the class for that cluster, which is given already. Form the pairs of cluster centroids made in the first and second part. The centroid of the second part nearest to a centroid of the first part will be paired. As the class of the first centroid is known, the same class can be assigned to the centroid of the cluster of the second part, which is unknown. The actual class of the patterns if known for the second part of the dataset can be used to test the classification accuracy of patterns in the second part. The proposed two-phase approach performs well in terms of classification accuracy and number of features selected on the given benchmarked datasets.

scholarly journals A novel approach for selective feature mechanism for two-phase intrusion detection system

2019 ◽  
Vol 14 (1) ◽  
pp. 101
Author(s):  
B Narendra Kumar ◽  
M S V Sivarama Bhadri Raju ◽  
B Vishnu Vardhan
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Performance Metrics ◽  
Detection System ◽  
Computational Time ◽  
Support Vector ◽  
Phase System ◽  
Second Phase ◽  
Two Phase ◽  
Linear Dependency

Intrusion Detection is an important aspect to secure the computing systems from different intrusions. To improve the accuracy and to reduce the computational time, this paper proposes a two-phase hybrid method based on the SVM and RNN. In addition, this paper also had a proposal to obtain a few sets of features with a feature selection technique in which the detection performance increases. For the two-phase system, two different feature selection techniques were proposed which solves both the linear dependency and non-linear dependency between the features. In the first phase, the RNN combines with the proposed Joint Mutual Information Maximization (JMIM) based feature selection and in the second phase, the Support Vector Machine (SVM) combines with correlation based feature selection. Extensive simulations are carried out over the proposed system using two different datasets, NSL-KDD and Kyoto2006+. The performance is measured through the performance metrics such as Detection Rate (DR), Precision, False Alarm Rate (FAR), Accuracy and F-Score. Furthermore, a comparative analysis with few recent hybrid frameworks is also enumerated. The obtained results signify the effectiveness of proposed method.

A Comparison of Tem and Apfim to the Interpretation of Modulated Microstructures

1985 ◽  
Vol 43 ◽  
pp. 70-71
Author(s):  
M.G. Burke ◽  
M.K. Miller
Keyword(s):  
Low Temperature ◽  
Microstructural Characterization ◽  
Superlattice Reflection ◽  
High Volume ◽  
Atom Probe ◽  
Dark Field ◽  
Miscibility Gap ◽  
Second Phase ◽  
Two Phase ◽  
Probe Field

Interpretation of fine-scale microstructures containing high volume fractions of second phase is complex. In particular, microstructures developed through decomposition within low temperature miscibility gaps may be extremely fine. This paper compares the morphological interpretations of such complex microstructures by the high-resolution techniques of TEM and atom probe field-ion microscopy (APFIM).The Fe-25 at% Be alloy selected for this study was aged within the low temperature miscibility gap to form a <100> aligned two-phase microstructure. This triaxially modulated microstructure is composed of an Fe-rich ferrite phase and a B2-ordered Be-enriched phase. The microstructural characterization through conventional bright-field TEM is inadequate because of the many contributions to image contrast. The ordering reaction which accompanies spinodal decomposition in this alloy permits simplification of the image by the use of the centered dark field technique to image just one phase. A CDF image formed with a B2 superlattice reflection is shown in fig. 1. In this CDF micrograph, the the B2-ordered Be-enriched phase appears as bright regions in the darkly-imaging ferrite. By examining the specimen in a [001] orientation, the <100> nature of the modulations is evident.

THE DYNAMIC FRACTURE OF TWO PHASE ALLOYS WITH SECOND PHASE INCLUSIONS

1985 ◽  
Vol 46 (C5) ◽  
pp. C5-251-C5-255
Author(s):  
S. Pytel ◽  
L. Wojnar
Keyword(s):  
Dynamic Fracture ◽  
Second Phase ◽  
Two Phase
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