Combining Bayesian Networks, k Nearest Neighbours Algorithm and Attribute Selection for Gene Expression Data Analysis

Sierra, Basilio; Lazkano, Elena; Martínez-Otzeta, José María; Astigarraga, Aitzol

doi:10.1007/978-3-540-30549-1_8

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…While combining classifiers complementary information can be gained by fusing the different sources. All those described combinations can produce appreciable improvements Sierra, Lazkano, Martinez-Otzeta, & Astigarraga, 2003).…”

Section: Algorithm Combination Methodologymentioning

confidence: 96%

Study of Activity Recognition Dataset Using Combined Probabilistic and Instance Based Algorithms

Dedabrashvili

2018

JTST

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Wide range of applications involve classification process where supervised learningapproaches play a significant role. Improvement of classification accuracy is one ofthe tasks which is most frequently carried out by researchers worldwide. This paperdescribes selected statistical and instance-based approaches and presents hybrid classifierfor Human Activity Recognition. Obtained results outperform solely applicationof each algorithm paradigm to the dataset and strengths the hypothesis for improvingclassification accuracy by using ensembles of classifiers.

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Section: Algorithm Combination Methodologymentioning

confidence: 96%

Study of Activity Recognition Dataset Using Combined Probabilistic and Instance Based Algorithms

Dedabrashvili

2018

JTST

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“…Improvements in classification can also be achieved by selecting and/or weighting features (see [28] for an example). Probabilistic voting approaches have also been used ( [21], [26]); the main idea here is that each case among the k nearest ones makes a weighted vote in favour of the class it belongs to, being the weight the probability each case has of belonging to its own class. A very well kown approach is the so called Instance Based Learning (IBL), based on the work of Aha [2] and Wettschereck [32]; there are several versions of the algorithm [1].…”

Section: Related Workmentioning

confidence: 99%

K Nearest Neighbor Equality: Giving equal chance to all existing classes

Sierra¹,

Lazkano²,

Irigoien³

et al. 2011

Information Sciences

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“…In 2013 the combination of Naive Bayes Classifier and K-Nearest Neighbor to predict the 12 positions of profitability of financial institutions in Bangladesh Country [17]. The classification algorithm combines several algorithms conducted in 2004, combining Bayesian network algorithms and K-Nearest Neighbors for data analysis, predicting cancer class classes into three DNA microarray datasets namely Colon, Leukemia and NCI-60 [18].…”

Section: Introductionmentioning

confidence: 99%

K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor

Safri¹,

Arifudin

Muslim

2018

SJI

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Health is a human right and one of the elements of welfare that must be realized in the form of giving various health efforts to all the people of Indonesia. Poverty in Indonesia has become a national problem and even the government seeks efforts to alleviate poverty. For example, poor families have relatively low levels of livelihood and health. One of the new policies of the Sakti Government Card Program issued by the government includes three cards, namely Indonesia Smart Card (KIP), Healthy Indonesia Card (KIS) and Prosperous Family Card (KKS). In this study to determine the feasibility of a healthy Indonesian card (KIS) required a method of optimal accuracy. The data used in this study is KIS data which amounts to 200 data records with 15 determinants of feasibility in 2017 taken at the Social Service of Pekalongan Regency. The data were processed using the K-Nearest Neighbor algorithm and the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm. This can be seen from the accuracy of determining the feasibility of K-Nearest Neighbor algorithm of 64%, while the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm is 96%, so the combination of K-Nearest Neighbor-Naive Bayes Classifier algorithm is the optimal algorithm in determining the feasibility of healthy Indonesian card recipients with an increase of 32% accuracy. This study shows that the accuracy of the results of determining feasibility using a combination of K-Nearest Neighbor-Naive Bayes Classifier algorithms is better than the K-Nearest Neighbor algorithm.

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Combining Bayesian Networks, k Nearest Neighbours Algorithm and Attribute Selection for Gene Expression Data Analysis

Cited by 7 publications

References 21 publications

Study of Activity Recognition Dataset Using Combined Probabilistic and Instance Based Algorithms

Study of Activity Recognition Dataset Using Combined Probabilistic and Instance Based Algorithms

K Nearest Neighbor Equality: Giving equal chance to all existing classes

K-Nearest Neighbor and Naive Bayes Classifier Algorithm in Determining The Classification of Healthy Card Indonesia Giving to The Poor

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