Findings for the Conducted Investigations

2021 ◽  
pp. 117-141
Keyword(s):  
Feature Selection ◽  
Gene Selection ◽  
Cancer Classification ◽  
Classification Algorithms ◽  
Third Phase ◽  
Original Dataset ◽  
Two Phases ◽  
Selection Algorithms ◽  
Gene Feature

This chapter focuses on the results produced from each case study experiment. For case one, the experiments were conducted in three phases. Phase one implemented GA, PSO, and IG as the gene/feature selection algorithms over the entire dataset. Phase =two2 utilised the original dataset to implement only the cancer classification algorithms without involving any gene/feature selection algorithms. Four recognised classification algorithms are employed: SVM, NB, GP, and DT. The third phase implemented the combined approach of gene selection and cancer classification algorithms. The results of these phases are presented in the next subsections. For case two, these experiments were implemented in two phases. Phase one implemented the classification algorithms over the features selected by the hybridised selection algorithms (GA+IG), whereas Phase two classified the features using the proposed two-stage multifilter selection system. In this section, the results are presented as follows

Research Approach With Machine Learning Underpinned

2021 ◽  
pp. 63-97
Keyword(s):  
Machine Learning ◽  
Colon Cancer ◽  
Feature Selection ◽  
High Performance ◽  
Classification Algorithms ◽  
Research Approach ◽  
Research Questions ◽  
Selection Algorithms ◽  
Gene Feature

This chapter describes several methodologies and proposed models used to examine the accuracy and efficiency of high-performance colon-cancer feature selection and classification algorithms to solve the problems identified in Chapter 2. An elaboration of the diverse methods of gene/feature selection algorithms and the related classification algorithms implemented throughout this study are presented. A prototypical methodology blueprint for each experiment is developed to answer the research questions in Chapter 1. Each system model is also presented, and the measures used to validate the performance of the model's outcome are discussed.

scholarly journals A Review on Time-domain Peak Detection and Classification Algorithms for Electroencephalogram Signals

Mekatronika ◽  
2019 ◽  
Vol 1 (2) ◽  
pp. 115-121
Author(s):  
Asrul Adam ◽  
Ammar Faiz Zainal Abidin ◽  
Zulkifli Md Yusof ◽  
Norrima Mokhtar ◽  
Mohd Ibrahim Shapiai
Keyword(s):  
Feature Selection ◽  
Time Domain ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Peak Detection ◽  
Classification Algorithm ◽  
Classification Algorithms ◽  
Classification Methods ◽  
Eeg Signals ◽  
Selection Algorithms

In this paper, the developments in the field of EEG signals peaks detection and classification methods based on time-domain analysis have been discussed. The use of peak classification algorithm has end up the most significant approach in several applications. Generally, the peaks detection and classification algorithm is a first step in detecting any event-related for the variation of signals. A review based on the variety of peak models on their respective classification methods and applications have been investigated. In addition, this paper also discusses on the existing feature selection algorithms in the field of peaks classification.

GENE SELECTION FOR CANCER CLASSIFICATION USING WRAPPER APPROACHES

2004 ◽  
Vol 18 (08) ◽  
pp. 1373-1390 ◽  
Author(s):  
ROSA BLANCO ◽  
PEDRO LARRAÑAGA ◽  
IÑAKI INZA ◽  
BASILIO SIERRA
Keyword(s):  
Gene Expression ◽  
Gene Selection ◽  
Cancer Classification ◽  
Feature Subset Selection ◽  
Feature Subset ◽  
Estimation Of Distribution ◽  
Distribution Algorithms ◽  
Selection Algorithms ◽  
Selection Of ◽  
General Method

Despite the fact that cancer classification has considerably improved, nowadays a general method that classifies known types of cancer has not yet been developed. In this work, we propose the use of supervised classification techniques, coupled with feature subset selection algorithms, to automatically perform this classification in gene expression datasets. Due to the large number of features of gene expression datasets, the search of a highly accurate combination of features is done by means of the new Estimation of Distribution Algorithms paradigm. In order to assess the accuracy level of the proposed approach, the naïve-Bayes classification algorithm is employed in a wrapper form. Promising results are achieved, in addition to a considerable reduction in the number of genes. Stating the optimal selection of genes as a search task, an automatic and robust choice in the genes finally selected is performed, in contrast to previous works that research the same types of problems.

Design and Procedures for the Investigation Conducted

2021 ◽  
pp. 98-116
Keyword(s):  
Colon Cancer ◽  
Time Complexity ◽  
High Performance ◽  
Gene Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Cancer Classification ◽  
Experimental Setup ◽  
Detailed Design

In this chapter, the design of each proposed case study model mentioned in Chapter 3 is presented with their different experimental procedures. The chapter includes the data preparation, suitable parameters and data pre-processing, and detailed design of two case studies. Case 1: examining the accuracy and efficiency (time complexity) of high-performance gene selection and cancer classification algorithms; Case 2: A two-stage hybrid multi-filter feature selection method for high colon-cancer classification. It shows the experimental setup and environment and the description of the hardware and software components used.

An Evolutionary Approach for Balancing Effectiveness and Representation Level in Gene Selection

2015 ◽  
Vol 8 (2) ◽  
pp. 16-33
Author(s):  
Nicoletta Dessì ◽  
Barbara Pes ◽  
Laura Maria Cannas
Keyword(s):  
Feature Selection ◽  
Classification Accuracy ◽  
Search Strategy ◽  
Gene Selection ◽  
Hybrid Methods ◽  
Large Body ◽  
Specific Model ◽  
Selection Algorithms ◽  
Microarray Datasets ◽  
Representation Level

As data mining develops and expands to new application areas, feature selection also reveals various aspects to be considered. This paper underlines two aspects that seem to categorize the large body of available feature selection algorithms: the effectiveness and the representation level. The effectiveness deals with selecting the minimum set of variables that maximize the accuracy of a classifier and the representation level concerns discovering how relevant the variables are for the domain of interest. For balancing the above aspects, the paper proposes an evolutionary framework for feature selection that expresses a hybrid method, organized in layers, each of them exploits a specific model of search strategy. Extensive experiments on gene selection from DNA-microarray datasets are presented and discussed. Results indicate that the framework compares well with different hybrid methods proposed in literature as it has the capability of finding well suited subsets of informative features while improving classification accuracy.

scholarly journals THE IDENTIFICATION OF DETERMINANT PARAMETER IN FOREST FIRE BASED ON FEATURE SELECTION ALGORITHMS

SINERGI ◽  
2019 ◽  
Vol 23 (3) ◽  
pp. 184
Author(s):  
Devi Fitrianah ◽  
Hisyam Fahmi
Keyword(s):  
Feature Selection ◽  
Forest Fire ◽  
Feature Selection Method ◽  
Selection Method ◽  
Weather Data ◽  
Selection Methods ◽  
Cloud Coverage ◽  
Average Accuracy ◽  
Selection Algorithms

This research conducts studies of the use of the Sequential Forward Floating Selection (SFFS) Algorithm and Sequential Backward Floating Selection (SBFS) Algorithm as the feature selection algorithms in the Forest Fire case study. With the supporting data that become the features of the forest fire case, we obtained information regarding the kinds of features that are very significant and influential in the event of a forest fire. Data used are weather data and land coverage of each area where the forest fire occurs. Based on the existing data, ten features were included in selecting the features using both feature selection methods. The result of the Sequential Forward Floating Selection method shows that earth surface temperature is the most significant and influential feature in regards to forest fire, while, based on the result of the Sequential Backward Feature Selection method, cloud coverage, is the most significant. Referring to the results from a total of 100 tests, the average accuracy of the Sequential Forward Floating Selection method is 96.23%. It surpassed the 82.41% average accuracy percentage of the Sequential Backward Floating Selection method.

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