Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Data Mining Practical Machine Learning Tools and Techniques

EvoCOMNET Contributions.- New Research in Nature Inspired Algorithms for Mobility Management in GSM Networks.- Adaptive Local Search for a New Military Frequency Hopping Planning Problem.- SS vs PBIL to Solve a Real-World Frequency Assignment Problem in GSM Networks.- Reconstruction of Networks from Their Betweenness Centrality.- A Self-learning Optimization Technique for Topology Design of Computer Networks.- A Comparative Study of Fuzzy Inference Systems, Neural Networks and Adaptive Neuro Fuzzy Inference Systems for Portscan Detection.- EvoFIN Contributions.- Evolutionary Single-Position Automated Trading.- Genetic Programming in Statistical Arbitrage.- Evolutionary System for Generating Investment Strategies.- Horizontal Generalization Properties of Fuzzy Rule-Based Trading Models.- Particle Swarm Optimization for Tackling Continuous Review Inventory Models.- Option Model Calibration Using a Bacterial Foraging Optimization Algorithm.- A SOM and GP Tool for Reducing the Dimensionality of a Financial Distress Prediction Problem.- Quantum-Inspired Evolutionary Algorithms for Financial Data Analysis.- EvoHOT Contributions.- Analysis of Reconfigurable Logic Blocks for Evolvable Digital Architectures.- Analogue Circuit Control through Gene Expression.- Discovering Several Robot Behaviors through Speciation.- Architecture Performance Prediction Using Evolutionary Artificial Neural Networks.- Evolving a Vision-Driven Robot Controller for Real-World Indoor Navigation.- Evolving an Automatic Defect Classification Tool.- Deterministic Test Pattern Generator Design.- An Evolutionary Methodology for Test Generation for Peripheral Cores Via Dynamic FSM Extraction.- Exploiting MOEA to Automatically Geneate Test Programs for Path-Delay Faults in Microprocessors.- EvoIASP Contributions.- Evolutionary Object Detection by Means of Naive Bayes Models Estimation.- An Evolutionary Framework for Colorimetric Characterization of Scanners.- Artificial Creatures for Object Tracking and Segmentation.- Automatic Recognition of Hand Gestures with Differential Evolution.- Optimizing Computed Tomographic Angiography Image Segmentation Using Fitness Based Partitioning.- A GA-Based Feature Selection Algorithm for Remote Sensing Images.- An Evolutionary Approach for Ontology Driven Image Interpretation.- Hybrid Genetic Algorithm Based on Gene Fragment Competition for Polyphonic Music Transcription.- Classification of Seafloor Habitats Using Genetic Programming.- Selecting Local Region Descriptors with a Genetic Algorithm for Real-World Place Recognition.- Object Detection Using Neural Networks and Genetic Programming.- Direct 3D Metric Reconstruction from Multiple Views Using Differential Evolution.- Discrete Tomography Reconstruction through a New Memetic Algorithm.- A Fuzzy Hybrid Method for Image Decomposition Problem.- Triangulation Using Differential Evolution.- Fast Multi-template Matching Using a Particle Swarm Optimization Algorithm for PCB Inspection.- EvoMUSART Contributions.- A Generative Representation for the Evolution of Jazz Solos.- Automatic Invention of Fitness Functions with Application to Scene Generation.- Manipulating Artificial Ecosystems.- Evolved Diffusion Limited Aggregation Compositions.- Scaffolding for Interactively Evolving Novel Drum Tracks for Existing Songs.- AtomSwarm: A Framework for Swarm Improvisation.- Using DNA to Generate 3D Organic Art Forms.- Towards Music Fitness Evaluation with the Hierarchical SOM.- Evolutionary Pointillist Modules: Evolving Assemblages of 3D Objects.- An Artificial-Chemistry Approach to Generating Polyphonic Musical Phrases.- Implicit Fitness Functions for Evolving a Drawing Robot.- Free Flight in Parameter Space: A Dynamic Mapping Strategy for Expressive Free Impro.- Modelling Video Games' Landscapes by Means of Genetic Terrain Programming - A New Approach for Improving Users' Experience.- Virtual Constructive Swarm Compositions and Inspirations.- New-Generation Methods in an Interpolating EC Synthesizer Interface.- Composing Music with Neural Networks and Probabilistic Finite-State Machines.- TransFormer #13: Exploration and Adaptation of Evolution Expressed in a Dynamic Sculpture.- EvoNUM Contributions.- Multiobjective Tuning of Robust PID Controllers Using Evolutionary Algorithms.- Truncation Selection and Gaussian EDA: Bounds for Sustainable Progress in High-Dimensional Spaces.- Scalable Continuous Multiobjective Optimization with a Neural Network-Based Estimation of Distribution Algorithm.- Cumulative Step Length Adaptation for Evolution Strategies Using Negative Recombination Weights.- Computing Surrogate Constraints for Multidimensional Knapsack Problems Using Evolution Strategies.- A Critical Assessment of Some Variants of Particle Swarm Optimization.- An Evolutionary Game-Theoretical Approach to Particle Swarm Optimisation.- A Hybrid Particle Swarm Optimization Algorithm for Function Optimization.- EvoSTOC Contributions.- Memory Based on Abstraction for Dynamic Fitness Functions.- A Memory Enhanced Evolutionary Algorithm for Dynamic Scheduling Problems.- Compound Particle Swarm Optimization in Dynamic Environments.- An Evolutionary Algorithm for Adaptive Online Services in Dynamic Environment.- EvoTHEORY Contributions.- A Study of Some Implications of the No Free Lunch Theorem.- Negative Slope Coefficient and the Difficulty of Random 3-SAT Instances.- EvoTRANSLOG Contributions.- A Memetic Algorithm for the Team Orienteering Problem.- Decentralized Evolutionary Optimization Approach to the p-Median Problem.- Genetic Computation of Road Network Design and Pricing Stackelberg Games with Multi-class Users.- Constrained Local Search Method for Bus Fleet Scheduling Problem with Multi-depot with Line Change.- Evolutionary System with Precedence Constraints for Ore Harbor Schedule Optimization.

Applications of evolutionary computing: EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Naples, Italy, March 26-28, 2008. Proceedings.

An enhanced genetic algorithm (EGA) is proposed to reduce text dimensionality.The proposed EGA outperformed the traditional genetic algorithm.The EGA is incorporated with six filter feature selection methods to create hybrid feature selection approaches.The proposed hybrid approaches outperformed the single filtering methods. This paper proposes hybrid feature selection approaches based on the Genetic Algorithm (GA). This approach uses a hybrid search technique that combines the advantages of filter feature selection methods with an enhanced GA (EGA) in a wrapper approach to handle the high dimensionality of the feature space and improve categorization performance simultaneously. First, we propose EGA by improving the crossover and mutation operators. The crossover operation is performed based on chromosome (feature subset) partitioning with term and document frequencies of chromosome entries (features), while the mutation is performed based on the classifier performance of the original parents and feature importance. Thus, the crossover and mutation operations are performed based on useful information instead of using probability and random selection. Second, we incorporate six well-known filter feature selection methods with the EGA to create hybrid feature selection approaches. In the hybrid approach, the EGA is applied to several feature subsets of different sizes, which are ranked in decreasing order based on their importance, and dimension reduction is carried out. The EGA operations are applied to the most important features that had the higher ranks. The effectiveness of the proposed approach is evaluated by using naive Bayes and associative classification on three different collections of Arabic text datasets. The experimental results show the superiority of EGA over GA, comparisons of GA with EGA showed that the latter achieved better results in terms of dimensionality reduction, time and categorization performance. Furthermore, six proposed hybrid FS approaches consisting of a filter method and the EGA are applied to various feature subsets. The results showed that these hybrid approaches are more effective than single filter methods for dimensionality reduction because they were able to produce a higher reduction rate without loss of categorization precision in most situations.

Hybrid feature selection based on enhanced genetic algorithm for text categorization

Dragonfly Algorithm (DA) is a recent swarm-based optimization method that imitates the hunting and migration mechanisms of idealized dragonflies. Recently, a binary DA (BDA) has been proposed. During the algorithm iterative process, the BDA updates its five main coefficients using random values. This updating mechanism can be improved to utilize the survival-of-the-fittest principle by adopting different functions such as linear, quadratic, and sinusoidal. In this paper, a novel BDA is proposed. The algorithm uses different strategies to update the values of its five main coefficients to tackle Feature Selection (FS) problems. Three versions of BDA have been proposed and compared against the original DA. The proposed algorithms are Linear-BDA, Quadratic-BDA, and Sinusoidal-BDA. The algorithms are evaluated using 18 well-known datasets. Thereafter, they are compared in terms of classification accuracy, the number of selected features, and fitness value. The results show that Sinusoidal-BDA outperforms other proposed methods in almost all datasets. Furthermore, Sinusoidal-BDA exceeds three swarm-based methods in all the datasets in terms of classification accuracy and it excels in most datasets when compared in terms of the fitness function value. In a nutshell, the proposed Sinusoidal-BDA outperforms the comparable feature selection algorithms and the proposed updating mechanism has a high impact on the algorithm performance when tackling FS problems.

An improved Dragonfly Algorithm for feature selection

Text classification is one of the challenging computational tasks in machine learning community due to the increased amounts of natural language text documents available in the electronic forms. In this process, feature selection (FS) is an essential phase because thousands of possible feature sets may be considered in text classification. This paper proposes an enhanced binary grey wolf optimizer (GWO) within a wrapper FS approach to tackle Arabic text classification problems. The proposed binary GWO is utilized to play the role of a wrapper-based feature selection technique. The performance of the proposed method using different learning models, including decision trees, K-nearest neighbour, Naive Bayes, and SVM classifiers, are investigated. Three Arabic public datasets, namely Alwatan, Akhbar-Alkhaleej, and Al-jazeera-News, are utilized to evaluate the efficacy of different BGWO-based wrapper methods. Results and analysis show that SVM-based feature selection technique with the proposed binary GWO optimizer with elite-based crossover scheme has enhanced efficacy in dealing with Arabic text classification problems compared to other peers.

Feature selection using binary grey wolf optimizer with elite-based crossover for Arabic text classification

Document categorization is an important topic that is central to many applications that demand reasoning about and organisation of text documents, web pages, and so forth. Document classification is commonly achieved by choosing appropriate features (terms) and building a term-frequency inerse-document frequency (TFIDF) feature vector. In this process, feature selection is a key factor in the accuracy and effectiveness of resulting classifications. For a given task, the right choice of features means accurate classification with suitable levels of computational efficiency. Meanwhile, most document classification work is based on English language documents. In this paper we make three main contributions: (i) we demonstrate successful document classification in the context of Arabic documents (although previous work has demonstrated text classification in Arabic, the datasets used, and the experimental setup, have not been revealed); (ii) we offer our datasets to enable other researchers to compare directly with our results; (iii) we demonstrate a combination of Binary PSO and K nearest neighbour that performs well in selecting good sets of features for this task.

/pdf/feature-subset-selection-for-arabic-document-categorization-9dcivrc57m.pdf

Feature subset selection for Arabic document categorization using BPSO-KNN

Software fault prediction (SFP) is a challenging process that any successful software should go through it to make sure that all software components are free of faults. In general, soft computing and machine learning methods are useful in tackling this problem. The size of fault data is usually huge since it is obtained from mining software historical repositories. This data consists of a large number of features (metrics). Determining the most valuable features (i.e., Feature Selection (FS) is an excellent solution to reduce data dimensionality. In this paper, we proposed an enhanced version of the Whale Optimization Algorithm (WOA) by combining it with a single point crossover method. The proposed enhancement helps the WOA to escape from local optima by enhancing the exploration process. Five different selection methods are employed: Tournament, Roulette wheel, Linear rank, Stochastic universal sampling, and random-based. To evaluate the performance of the proposed enhancement, 17 available SFP datasets are adopted from the PROMISE repository. The deep analysis shows that the proposed approach outperformed the original WOA and the other six state-of-the-art methods, as well as enhanced the overall performance of the machine learning classifier.

/pdf/boosted-whale-optimization-algorithm-with-natural-selection-2iwsql5qrb.pdf

Boosted Whale Optimization Algorithm With Natural Selection Operators for Software Fault Prediction

In the feature selection process, reaching the best subset of features is considered a difficult task. To deal with the complexity associated with this problem, a sophisticated and robust optimization approach is needed. This paper proposes an efficient feature selection approach based on a Boolean variant of Particle Swarm Optimization (BPSO) boosted with Evolutionary Population Dynamics (EPD). The proposed improvement assists the BPSO to avoid local optima obstacles via boosting its exploration ability. In the BPSO-EPD, the worst half of the solutions are discarded by repositioning them around the optimal solutions selected from the best half. Six natural selection mechanisms comprising Best-based, Tournament, Roulette wheel, Stochastic universal sampling, Linear rank, and Random-based are employed to select guiding solutions. To assess the performance of the proposed improvement, 22 well-regarded datasets collected from the UCI repository are employed. The experimental results demonstrate the superiority of the proposed EPD-based feature selection approaches, especially the BPSO-TEPD variant when compared with conventional BPSO and other five EPD-based variants. Taking SpecEW dataset as an example, an increment of 6.7% accuracy can be achieved for BSPO-TEPD. Consequently, BPSO-TEPD approach also outperformed other well-known optimizers, including two binary variants of PSO using S-shaped transfer function (SBPSO) and V-shaped transfer function (VBPSO), Binary Grasshopper Optimization Algorithm (BGOA), Binary Gravitational Search Algorithm (BGSA), Binary Ant Lion Optimizer (BALO), Binary Bat algorithm (BBA), Binary Salp Swarm Algorithm (BSSA), Binary Whale Optimization Algorithm (BWOA), and Binary Teaching-Learning Based Optimization (BTLBO). The result emphasizes the excellent behavior of EPD strategies in evolving the ability of BPSO when dealing with feature selection problems. 

Boolean Particle Swarm Optimization with various Evolutionary Population Dynamics approaches for feature selection problems

There are various fields are affected by the growth of data dimensionality The major problems which are resulted from high dimensionality of data including high memory requirements, high computational cost, and low machine learning classifier performance Therefore, proper selection of relevant features from the set of available features and the removal of irrelevant features will solve these problems Therefore, to solve the feature selection problem, an improved version of Dragonfly Algorithm (DA) is proposed by combining it with Simulated Annealing (SA), where the improved algorithm named BDA-SA To solve the local optima problem of DA and enhance its ability in selecting the best subset of features for classification problems, Simulated Annealing (SA) was applied to the best solution found by Binary Dragonfly algorithm in attempt to improve its accuracy A set of frequently used data sets from UCI repository was utilized to evaluate the performance of the proposed FS approach Results show that the proposed hybrid approach, named BDA-SA, has superior performance when compared to wrapper-based FS methods including a feature selection method based on the basic version of Binary Dragonfly Algorithm

/pdf/hybrid-binary-dragonfly-algorithm-with-simulated-annealing-c9w29aewro.pdf

Hamouda Chantar

Papers

Feature selection using binary grey wolf optimizer with elite-based crossover for Arabic text classification

Feature subset selection for Arabic document categorization using BPSO-KNN

Boosted Whale Optimization Algorithm With Natural Selection Operators for Software Fault Prediction

Boolean Particle Swarm Optimization with various Evolutionary Population Dynamics approaches for feature selection problems

Hybrid Binary Dragonfly Algorithm with Simulated Annealing for Feature Selection