Improve the Classifier Accuracy for Continuous Attributes in Biomedical Datasets Using a New Discretization Method

Moulai

2020

Vietnam J. Comp. Sci.

In this paper, for the¯rst time, a novel discretization scheme is proposed aiming at enabling scalability but also at least three other strong challenges. It is based on a Left-to-Right (LR) scanning process, which partitions the input stream into intervals. This task can be implemented by an algorithm or by using a generator that builds automatically the discretization program. We focus especially on unsupervised discretization and design a method called Usupervised Left to Right Discretization (ULR-Discr). Extensive experiments were conducted using various cut-point functions on small, large and medical public datasets. First, ULR-Discr variants under di®erent statistics are compared between themselves with the aim at observing the impact of the cut-point functions on accuracy and runtime. Then the proposed method is compared to traditional and recent techniques for classi¯cation. The result is that the classi¯cation accuracy is highly improved when using our method for discretization.

“…The authors of Ref. 16 designed an unsupervised algorithm called ZDisc. It is based on the z-score standard deviation technique for continuous attributes on biomedical datasets.…”

Section: Related Workmentioning

confidence: 99%

“…As evoked previously, unsupervised discretization methods are less present in the literature than the supervised ones. The most recent algorithm we found is called ZDisc 16 and was published in 2014. We used this reference for the comparison with our algorithm.…”

Section: Comparison Between Ulr-discr and Recent State-of-the-art Metmentioning

confidence: 99%

An Automated Unsupervised Discretization Method: A Novel Approach

Moulai

2020

Vietnam J. Comp. Sci.

“…In order to train the Maximum Entropy model with a very limited training dataset, we need to convert attributes that have continuous numeric values into discrete ones. There has been a lot of research done on continuous feature discretization field [27][28][29][30][31][32]. Methods for discretization are broadly classified into Supervised vs. Unsupervised, Global vs. Local, and Static vs.…”

Section: K-means Clusteringmentioning

confidence: 99%

Predicting the Outcome of NBA Playoffs Based on the Maximum Entropy Principle

Cheng

Zhang

Kyebambe

et al. 2016

Entropy

Predicting the outcome of National Basketball Association (NBA) matches poses a challenging problem of interest to the research community as well as the general public. In this article, we formalize the problem of predicting NBA game results as a classification problem and apply the principle of Maximum Entropy to construct an NBA Maximum Entropy (NBAME) model that fits to discrete statistics for NBA games, and then predict the outcomes of NBA playoffs using the model. Our results reveal that the model is able to predict the winning team with 74.4% accuracy, outperforming other classical machine learning algorithms that could only afford a maximum prediction accuracy of 70.6% in the experiments that we performed.

2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)

“…An approach to handle medical datasets consisting mixed attribute types is handled in [5]. Some of the research contributions in missing values include [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

Section: Related Workmentioning

confidence: 99%

“…Alternately, we may apply the existing imputation algorithms, and fix missing values. Some significant novel approaches include [6][7][8][12][13][14]. In this paper, we propose an approach to impute missing values existing in medical records.…”

Section: B Handling and Imputing Missing Valuesmentioning

confidence: 99%

A novel approach for imputation of missing values for mining medical datasets

UshaRani¹,

Sammulal

2015

Imputation of missing attribute values in medical datasets for extracting hidden knowledge from medical datasets is an interesting research topic of interest which is very challenging. One cannot eliminate missing values in medical records. The reason may be because some tests may not been conducted as they are cost effective, values missed when conducting clinical trials, values may not have been recorded to name some of the reasons. Data mining researchers have been proposing various approaches to find and impute missing values. In this paper, we propose a novel imputation approach for fixing missing values. The approach is based on clustering concept and aims at dimensionality reduction of the records. This serves the need to use the same records of lower dimension to be used for clustering and classification of medical records to arrive at accurate decision prediction. The case study discussed shows that the missing values can be fixed and imputed efficiently by achieving dimensionality reduction. The proposed approach for imputation also achieved dimensionality reduction to perform efficient prediction of missing values.