Seir immune strategy for instance weighted naive bayes classification

© Springer International Publishing Switzerland 2015. Naive Bayes (NB) has been popularly applied in many classification tasks. However, in real-world applications, the pronounced advantage of NB is often challenged by insufficient training samples. Specifically, the high variance may occur with respect to the limited number of training samples. The estimated class distribution of a NB classier is inaccurate if the number of training instances is small. To handle this issue, in this paper, we proposed a SEIR (Susceptible, Exposed, Infectious and Recovered) immune-strategy-based instance weighting algorithm for naive Bayes classification, namely SWNB. The immune instance weighting allows the SWNB algorithm adjust itself to the data without explicit specification of functional or distributional forms of the underlying model. Experiments and comparisons on 20 benchmark datasets demonstrated that the proposed SWNB algorithm outperformed existing state-of-the-art instance weighted NB algorithm and other related computational intelligence methods

Self-adaptive attribute weighting for Naive Bayes classification

©2014 Elsevier Ltd. All rights reserved. Naive Bayes (NB) is a popular machine learning tool for classification, due to its simplicity, high computational efficiency, and good classification accuracy, especially for high dimensional data such as texts. In reality, the pronounced advantage of NB is often challenged by the strong conditional independence assumption between attributes, which may deteriorate the classification performance. Accordingly, numerous efforts have been made to improve NB, by using approaches such as structure extension, attribute selection, attribute weighting, instance weighting, local learning and so on. In this paper, we propose a new Artificial Immune System (AIS) based self-adaptive attribute weighting method for Naive Bayes classification. The proposed method, namely AISWNB, uses immunity theory in Artificial Immune Systems to search optimal attribute weight values, where self-adjusted weight values will alleviate the conditional independence assumption and help calculate the conditional probability in an accurate way. One noticeable advantage of AISWNB is that the unique immune system based evolutionary computation process, including initialization, clone, section, and mutation, ensures that AISWNB can adjust itself to the data without explicit specification of functional or distributional forms of the underlying model. As a result, AISWNB can obtain good attribute weight values during the learning process. Experiments and comparisons on 36 machine learning benchmark data sets and six image classification data sets demonstrate that AISWNB significantly outperforms its peers in classification accuracy, class probability estimation, and class ranking performance

STUDENTS DATA CLASSIFICATION MODEL

In this project, research is conducted based on data sets of undergraduates at varsity level to classify student performance data. The objective of the project is to develop a system that utilizes various intelligent techniques with targeted accuracy being at a minimal level of88%. The system is designed to predict students' CGPA upon graduation. Any further actions that can be taken to avoid students' dismissals, or to strengthen their area of interest or expertise can be derived from the outcome of this intelligent system. The project is implemented using data sets Iris and Student. Techniques used to support classification are separated into two different subprojects: (1) Back propagation feed forward neural network using Bayes probability to initialize weights, and (2) Fuzzy system. The proposed optimization of neural network and Bayes Theorem returns 92.55% level of accuracy for the student data. Further improvements can be performed on areas such as the individual variations of each technique and the combination of all three techniques to optimize accuracy. The project contributes in customizing a grading system for Universiti Teknologi PETRONAS. This system structure is generally relevant to many universities in Malaysia as they adopt a fairly similar approach in gradin

Learning-Assisted Automated Reasoning with Flyspeck

The considerable mathematical knowledge encoded by the Flyspeck project is combined with external automated theorem provers (ATPs) and machine-learning premise selection methods trained on the proofs, producing an AI system capable of answering a wide range of mathematical queries automatically. The performance of this architecture is evaluated in a bootstrapping scenario emulating the development of Flyspeck from axioms to the last theorem, each time using only the previous theorems and proofs. It is shown that 39% of the 14185 theorems could be proved in a push-button mode (without any high-level advice and user interaction) in 30 seconds of real time on a fourteen-CPU workstation. The necessary work involves: (i) an implementation of sound translations of the HOL Light logic to ATP formalisms: untyped first-order, polymorphic typed first-order, and typed higher-order, (ii) export of the dependency information from HOL Light and ATP proofs for the machine learners, and (iii) choice of suitable representations and methods for learning from previous proofs, and their integration as advisors with HOL Light. This work is described and discussed here, and an initial analysis of the body of proofs that were found fully automatically is provided

Evolutionary Algorithms for Hyperparameter Search in Machine Learning

Machine learning algorithms usually have a number of hyperparameters. The choice of values for these hyperparameters may have a significant impact on the performance of an algorithm. In practice, for most learning algorithms the hyperparameter values are determined empirically, typically by search. From the research that has been done in this area, approaches for automating the search of hyperparameters mainly fall into the following categories: manual search, greedy search, random search, Bayesian model-based optimization, and evolutionary algorithm-based search. However, all these approaches have drawbacks — for example, manual and random search methods are undirected, greedy search is very inefficient, Bayesian model-based optimization is complicated and performs poorly with large numbers of hyperparameters, and classic evolutionary algorithm-based search can be very slow and risks falling into local optima. In this thesis we introduce three improved evolutionary algorithms applied to search for high-performing hyperparameter values for different learning algorithms. The first, named EWLNB, combines Naive Bayes and lazy instance-weighted learning. The second, EMLNB, extends this approach to multiple label classification. Finally, we further develop similar methods in an algorithm, named SEODP, for optimizing hyperparameters of deep networks, and report its usefulness on a real-world application of machine learning for philanthropy. EWLNB is a differential evolutionary algorithm which can automatically adapt to different datasets without human intervention by searching for the best hyperparameters for the models based on the characteristics of the datasets to which it is applied. To validate the EWLNB algorithm, we first use it to optimize two key parameters for a locally-weighted Naive Bayes model. Experimental evaluation of this approach on 56 of the benchmark UCI machine learning datasets demonstrate that EWLNB significantly outperforms Naive Bayes as well as several other improved versions of the Naive Bayes algorithms both in terms of classification accuracy and class probability estimation. We then extend the EWLNB approach in the form of the Evolutionary Multi-label Lazy Naive Bayes (EMLNB) algorithm to enable hyperparameter search for multi-label classification problems. Lastly, we revise the above algorithms to propose a method, SEODP, for optimizing deep learning (DL) architecture and hyperparameters. SEODP uses a semi-evolutionary and semi-random approach to search for hyperparameter values, which is designed to evolve a solution automatically over different datasets. SEODP is much faster than other methods, and can adaptively determine different deep network architectures automatically. Experimental results show that compared with manual search, SEODP is much more effective, and compared with grid search, SEODP can achieve optimal performance using only approximately 2% of the running time of greedy search. We also use SEODP on a real-world social-behavioral dataset from a charity organization for a philanthropy application. This dataset contains comprehensive real-time attributes on potential indicators for candidates to be donors. The results show that SEODP is a promising approach for optimizing deep network (DN) architectures over different types of datasets, including a real-world dataset. In summary, the results in this thesis indicate that our methods address the main drawback of evolutionary algorithms, which is the convergence time, and show experimentally that evolutionary-based algorithms can achieve good results in optimizing the hyperparameters for a range of different machine learning algorithms

Sample efficiency, transfer learning and interpretability for deep reinforcement learning

Deep learning has revolutionised artificial intelligence, where the application of increased compute to train neural networks on large datasets has resulted in improvements in real-world applications such as object detection, text-to-speech synthesis and machine translation. Deep reinforcement learning (DRL) has similarly shown impressive results in board and video games, but less so in real-world applications such as robotic control. To address this, I have investigated three factors prohibiting further deployment of DRL: sample efficiency, transfer learning, and interpretability. To decrease the amount of data needed to train DRL systems, I have explored various storage strategies and exploration policies for episodic control (EC) algorithms, resulting in the application of online clustering to improve the memory efficiency of EC algorithms, and the maximum entropy mellowmax policy for improving the sample efficiency and final performance of the same EC algorithms. To improve performance during transfer learning, I have shown that a multi-headed neural network architecture trained using hierarchical reinforcement learning can retain the benefits of positive transfer between tasks while mitigating the interference effects of negative transfer. I additionally investigated the use of multi-headed architectures to reduce catastrophic forgetting under the continual learning setting. While the use of multiple heads worked well within a simple environment, it was of limited use within a more complex domain, indicating that this strategy does not scale well. Finally, I applied a wide range of quantitative and qualitative techniques to better interpret trained DRL agents. In particular, I compared the effects of training DRL agents both with and without visual domain randomisation (DR), a popular technique to achieve simulation-to-real transfer, providing a series of tests that can be applied before real-world deployment. One of the major findings is that DR produces more entangled representations within trained DRL agents, indicating quantitatively that they are invariant to nuisance factors associated with the DR process. Additionally, while my environment allowed agents trained without DR to succeed without requiring complex recurrent processing, all agents trained with DR appear to integrate information over time, as evidenced through ablations on the recurrent state.Open Acces

ENRICHING COMMUNICATION BETWEEN HUMANS AND AI AGENTS

Equipping AI agents with effective, human-compatible communication capabilities is pivotal to enabling them to effectively serve and aid humans. On one hand, agents should understand humans, being able to infer intentions and extract knowledge from language utterances. On the other hand, they should also help humans understand them, conveying (un)certainties and proactively consulting humans when facing difficult situations. This dissertation presents new training and evaluation frameworks that enrich communication between humans and AI agents. These frameworks improve two capabilities of an agent: (1) the ability to learn through natural communication with humans and (2) the ability to request and interpret information from humans during task execution. Regarding the first capability, I study the possibility and challenges of training agents with noisy human ratings. Providing humans with more expressive tools for teaching agents, I propose a framework that employs descriptive language as the teaching medium. On the second capability, I introduce new benchmarks that evaluate an agent’s ability to exchange information with humans to successfully perform indoor navigation tasks. On these benchmarks, I build agents that are capable of requesting rich, contextually useful information and show that they significantly outperform those without such capability. I conclude the dissertation with discussions on how to develop more sophisticated communication capabilities for agents