Large scale instance selection by means of federal instance selection

Cluster-oriented instance selection for classification problems

Instance selection in medical datasets: A divide-and-conquer framework

On learning dual classifiers for better data classification

The traditional evolutionary instance selection algorithm has the risk of redundant and noise training samples in the training set selection, which affects the classification effect. In this paper, instance selection, instance weighting and feature weighting are integrated into the cooperative coevolution framework, and a cooperative coevolutionary algorithm for KNN training set optimization selection is proposed. The CHC algorithm based on multi-point crossover strategy is used to further improve the accuracy of instance selection. The SSGA algorithm based on fast mutation strategy of instance weighting and feature weighting is synergistic with the instance selection, and it helps to remove noise and irrelevant data in the process of instance selection. The proposed method can speed up the convergence of the population, it can also improve the efficiency of the algorithm, and improve the KNN classification performance. The experimental results show that this method has advantages in classification accuracy and efficiency compared with some current evolutionary instance selection algorithms.

The paper presents bagging ensembles of instance selection algorithms. We use bagging to improve instance selection. The improvement comprises data compression and prediction accuracy. The examined instance selection algorithms for classification are ENN, CNN, RNG and GE and for regression are the developed by us Generalized CNN and Generalized ENN algorithms. Results of the comparative experimental study performed using different configurations on several datasets shows that the approachbased on bagging allowed for significant improvement, especially in terms of data compression.

Instance selection plays an important role in improving scalability of data mining algorithms, but it can also be used to improve the quality of the data mining results. In this dissertation we present a new optimization-based approach for instance selection that uses a genetic algorithm (GA) to select a subset of instances to produce a simpler decision tree with acceptable accuracy. The resultant trees are likely to be easier to comprehend and interpret by the decision maker and hence more useful in practice. We present numerical results for several difficult test datasets that indicate that GA-based instance selection can often reduce the size of the decision tree by an order of magnitude while still maintaining good prediction accuracy. The results suggest that GA-based instance selection works best for low entropy datasets. With higher entropy, there will be less benefit from instance selection. A comparison between GA and other heuristic approaches such as Rmhc (Random Mutation Hill Climbing) and simple construction heuristic, indicates that GA is able to obtain a good solution with low computation cost even for some large datasets. One advantage of instance selection is that it is able to increase the average instances associated with the leaves of the decision trees to avoid overfitting, thus instance selection can be used as an effective alternative to prune decision trees. Finally, the analysis on the selected instances reveals that instance selection helps to reduce outliers, reduce missing values, and select the most useful instances for separating classes.

Performing instance selection prior to the classifier training is always beneficial in terms of computational complexity reduction of the classifier training and sometimes also beneficial in terms of improving prediction accuracy. Removing the noisy instances improves the prediction accuracy and removing redundant and irrelevant instances does not negatively effect it. However, in practice the instance selection methods usually also remove some instances, which should not be removed from the training dataset, what results in decreasing the prediction accuracy. We discuss two methods to deal with the problem. The first method is the parameterization of instance selection algorithms, which allows to choose how aggressively the instances are removed and the second one is to embed the instance selection directly into the prediction model, which in our case is an MLP neural network.

SVOIS: Support Vector Oriented Instance Selection for text classification

This paper is an continuation of the accompanying paper with the same main title. The first paper reviewed instance selection algorithms, here results of empirical comparison and comments are presented. Several test were performed mostly on benchmark data sets from the machine learning repository at UCI. Instance selection algorithms were tested with neural networks and machine learning algorithms.

Instance selection is a popular preprocessing task in knowledge discovery and data mining. Its purpose is to reduce the size of data sets maintaining their predictive capabilities. The usual emerging problem at this point is that these methods quite often suffer of high computational complexity, which becomes highly inconvenient for processing huge data sets. In this paper, a parallel implementation for the instance selection algorithm Democratic Instance Selection (DIS) is presented. The main advantages of the DIS algorithm turn out to be its computational complexity, linear in the number of instances, as well as its internal structure, intuitively parallelizable. The purpose of this paper is threefold: firstly, the design of the DIS algorithm by following the MapReduce model; secondly, its implementation in the popular big data framework Spark; and finally, its empirical comparison over large-scale data sets. The results show that the processing time is reduced in a linear manner as the number of Spark executors increases, what makes it suitable for big data applications. In addition, the algorithm is publicly accessible to the scientific community.

A distributed evolutionary based instance selection algorithm for big data using Apache Spark

IFSB-ReliefF: A New Instance and Feature Selection Algorithm Based on ReliefF

The average volume of data produced daily is estimated to be over 2.5 quintillion byte. Moreover, by year 2020, it is estimated that 1.79MB of data will be created every second by each person in the world. Apparently, big datasets contain tremendous amount of valuable information that can be used for improved decision making. However, big data requires incredible amount of storage and computational resources for effective processing. Machine Learning (ML) algorithms are effective tools popularly used to analyze and extract concealed insights from datasets. However, some ML algorithms were not originally designed to handle big datasets, hence their computational complexity decreases with increase in data size. Consequently, this makes big data analytics extremely slow or unrealistic. Therefore, there is an obvious need for fast and effective techniques for big data analytics. This paper introduces an intelligent hybrid ML-based technique suitable for big data analytics (called EDISA_ML). EDISA_ML is a boundary detection and instance selection algorithm, inspired by edge detection in image processing. It was evaluated on four ML algorithms and big datasets, and the results show that it achieved a storage reduction of over 50% and simultaneously improved the training speed of the evaluated ML algorithms by over 93% (in some cases), without meaningfully affecting their prediction accuracy.

Unlocking massively parallel spectral proper orthogonal decompositions in the PySPOD package

Evolutionary stratified training set selection for extracting classification rules with trade off precision-interpretability