Big Data Analytics (BDA) is progressively becoming a popular practice implemented by many organizations, because of their potential to discover valuable in-sights for improved decision-making. The International Data Corporation predicts that the Global Datasphere will grow from 33 Zettabytes in 2018 to 175 Zettabytes in 2025. Obviously, we are currently in the era of Big Data (BD), and the rate of data growth is very alarming. Unfortunately, BD does not offer a lot of value in its unprocessed form. Therefore, to unlock the great potentials of BD, we need efficient BDA methods. Machine Learning (ML) algorithms are one of the most efficient tools suitable for data analytics, however, some ML algorithms cannot effectively handle BD; their computational complexity increases with in-crease in data size. Therefore, some researchers introduced various techniques for improving the speed of ML algorithms, including feature selection techniques, in-stance selection techniques, sampling, and distributed computing. However, most of them failed to achieve a balanced trade-off between storage reduction and predictive accuracy [1]. Therefore, this paper introduces a boundary detection and instance selection technique for improving the speed of ML-based BDA, called Ant Colony Optimization Instance Selection Algorithm for Machine Learning (ACOISA_ML)). The key highlights of ACOISA_ML are outlined below: Boundary identification: The first stage of ACOISA_ML is the boundary identification stage. Unlike other ACO-based instance selection techniques that directly use ACO algorithm for instance or feature selection, ACOISA_ML use ACO algorithm for boundary identification. It adopts the concept of ACO edge selection to search for different boundaries (not to select instances). To the best of author's knowledge, this study is one of the first studies that adopt the concept of ACO edge detection for instance selection problems. This concept is mostly used for image edge detection (and not data boundary identification) [2-4]. Boundary instance selection: The second stage of the proposed technique is the boundary instance selection stage. After identifying different boundaries, ACOISA selects the best boundary and use k-NN to select the relevant instances for training (that is, instances close to the best-identified boundary). Heuristic value computation: This study introduces a novel method for computing heuristic value for ACO. This method is suitable for boundary instance se-lection problems. ACOISA_ML is designed to use the proposed computation method to cal-
culate the heuristic value for each instance in the dataset. As afore-mentioned, ACO is used to identify the best boundary instance, that is, the in-stance with the highest pheromone value. Hence, the heuristic value for each in-stance is designed to reflect the boundary information for each instance.
The technique was evaluated on five ML algorithms, namely: (Artificial Neural
Network (ANN), Random Forest (RF), Naïve Bayes (NB), k Nearest Neighbor (k-NN),
and Logistic Regression (LR)). In this study, we refer to the models produced by the
full dataset as standard models, and the models produced by the reduced subset as
hybrid models. Finally, we compare the hybrid models to the standard models based
on the following criteria: (i) the ability to preserve prediction accuracy (ii) training
speed (iii) storage reduction percentage, and (iv) algorithm time (or instance selection
time). All the datasets used in this study were obtained from the UCI data repository
[
In addition, ACOISA_ML was compared to four recent instance selection algorithms, namely: LDIS, LSSM, LSBO, and ISDSP. The algorithms were evaluated on SVM, hence we first evaluated ACOISA_ML on SVM before comparing it to the algorithms. Annexure 3 shows the prediction accuracy (denoted as accuracy) and storage reduction percentage (denoted as storage) for the four algorithms. The best prediction accuracy for each dataset is underlined. As shown, ACOISA_ML outperformed LSSM in prediction accuracy in 6 out of 11 datasets and outperformed LSBO in 7 out of 11 datasets. Moreover, the results show that ACOISA_ML outperformed LDIS and ISDSP in 9 out of 11 datasets. Furthermore, T-test statistical analysis was performed to evaluate the speed improving capacity of ACOISA_ML. Specifically, we compared the training speed produced by the hybrid models of ANN and RF to the training speed produced by the standard algorithms. The P-values produced by all the test analysis are less than 0.05, hence we can conclude with 95% confidence level that ACOISA_ML is significantly faster, in terms of training speed, than the analyzed standard algorithms. Overall, the results show that the proposed technique is suit-able for fast and simplified BDA and ML speed optimization.
Keywords: Big data analytics, Machine learning, Instance selection, Data reduction, Speed optimization.
Annexures KNN Standard 0 0.02 0.01 0.03 0.02 0.04 0.06 0.03 0.02 0 Annexure 2. Average prediction accuracy for the hybrid and standard model
KNN ANN RF LR NB Standard Hybrid Standard Hybrid Standard Hybrid Standard Hybrid Standard Hybrid 90.55 86.86 88.5 85.165 83.75 82.1 91.05 88.085 79.6 78.705 95.725 90.995 80.975 79.39 77.375 75.9675 96.175 91.9125 62.3 62.2725 100 99.91 98.966 99.015 95.4825 99.97 100 99.95 90.8296 91.945 97.8297 93.7841 96.5498 93.4613 92.3205 86.9004 97.3845 90.384 89.4268 83.9232 96.0168 96.916 96.2361 97.328 96.4553 97.408 97.5333 97.948 90.846 92.764 99.9103 99.7752 99.7517 99.7062 96.8345 96.76 99.9931 99.9028 92.2069 92.5297 96.0911 94.6939 96.4109 94.7831 96.5566 95.3936 96.6881 95.1853 94.9611 93.2472 95.1171 93.7369 94.3199 93.2835 89.5366 86.871 93.3732 92.3119 76.7813 75.1022 97.7416 92.8845 89.8228 89.1367 89.8228 89.1367 96.5981 90.7919 82.1326 81.6352 80.24 81.8 83.84 85.2667 87.08 87.5167 85.24 85.5167 81.02 80.6083 Key: Standard: average prediction accuracy (%) produced by the standard algorithm, Hybrid: average prediction accuracy produced by the hybrid model Annexure 3. Comparison between ACOISA_ML and LDIS, LSSM, LSBO, ISDSP (SVM Classifier)
ACOISA_ML LDIS LSSM LSBO ISDSP Accuracy Storage Accuracy Storage Accuracy Storage Accuracy Storage Accuracy Storage 71.25 26.13 62 14 67 86 62 31 59 10 84.97 67.21 77 8 83 91 74 17 78 10 82.44 61.73 81 7 84 85 81 33 78 10 92.51 31.75 84 9 88 96 45 19 86 10 84.81 45.1 84 8 87 95 85 12 84 10 89.10 25 75 18 84 96 73 16 74 10 92.13 52.31 96 8 99 98 98 8 97 10 95.12 20.31 94 13 94 97 92 4 91 10 80.95 58.31 82 17 87 89 82 13 85 10 94.63 14.22 89 18 90 90 90 18 87 10 94.94 65.03 94 12 97 89 96 25 93 10 Key: Accuracy: average prediction accuracy (%) produced by the hybrid models, Storage: average storage reduction percentage produced by the instance selection techniques